AI Avatar For Zoom Meetings

AI Avatar For Zoom Meetings — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Connected-component labeling

Connected-component labeling (CCL), connected-component analysis (CCA), blob extraction, region labeling, blob discovery, or region extraction is an algorithmic application of graph theory, where subsets of connected components are uniquely labeled based on a given heuristic. Connected-component labeling is not to be confused with segmentation. Connected-component labeling is used in computer vision to detect connected regions in binary digital images, although color images and data with higher dimensionality can also be processed. When integrated into an image recognition system or human-computer interaction interface, connected component labeling can operate on a variety of information. Blob extraction is generally performed on the resulting binary image from a thresholding step, but it can be applicable to gray-scale and color images as well. Blobs may be counted, filtered, and tracked. Blob extraction is related to but distinct from blob detection. == Overview == A graph, containing vertices and connecting edges, is constructed from relevant input data. The vertices contain information required by the comparison heuristic, while the edges indicate connected 'neighbors'. An algorithm traverses the graph, labeling the vertices based on the connectivity and relative values of their neighbors. Connectivity is determined by the medium; image graphs, for example, can be 4-connected neighborhood or 8-connected neighborhood. Following the labeling stage, the graph may be partitioned into subsets, after which the original information can be recovered and processed . == Definition == The usage of the term connected-component labeling (CCL) and its definition is quite consistent in the academic literature, whereas connected-component analysis (CCA) varies both in terminology and in its definition of the problem. Rosenfeld et al. define connected components labeling as the “[c]reation of a labeled image in which the positions associated with the same connected component of the binary input image have a unique label.” Shapiro et al. define CCL as an operator whose “input is a binary image and [...] output is a symbolic image in which the label assigned to each pixel is an integer uniquely identifying the connected component to which that pixel belongs.” There is no consensus on the definition of CCA in the academic literature. It is often used interchangeably with CCL. A more extensive definition is given by Shapiro et al.: “Connected component analysis consists of connected component labeling of the black pixels followed by property measurement of the component regions and decision making.” The definition for connected-component analysis presented here is more general, taking the thoughts expressed in into account. == Algorithms == The algorithms discussed can be generalised to arbitrary dimensions, albeit with increased time and space complexity. === One component at a time === This is a fast and very simple method to implement and understand. It is based on graph traversal methods in graph theory. In short, once the first pixel of a connected component is found, all the connected pixels of that connected component are labelled before going onto the next pixel in the image. This algorithm is part of Vincent and Soille's watershed segmentation algorithm, other implementations also exist. In order to do that a linked list is formed that will keep the indexes of the pixels that are connected to each other, steps (2) and (3) below. The method of defining the linked list specifies the use of a depth or a breadth first search. For this particular application, there is no difference which strategy to use. The simplest kind of a last in first out queue implemented as a singly linked list will result in a depth first search strategy. It is assumed that the input image is a binary image, with pixels being either background or foreground and that the connected components in the foreground pixels are desired. The algorithm steps can be written as: Start from the first pixel in the image. Set current label to 1. Go to (2). If this pixel is a foreground pixel and it is not already labelled, give it the current label and add it as the first element in a queue, then go to (3). If it is a background pixel or it was already labelled, then repeat (2) for the next pixel in the image. Pop out an element from the queue, and look at its neighbours (based on any type of connectivity). If a neighbour is a foreground pixel and is not already labelled, give it the current label and add it to the queue. Repeat (3) until there are no more elements in the queue. Go to (2) for the next pixel in the image and increment current label by 1. Note that the pixels are labelled before being put into the queue. The queue will only keep a pixel to check its neighbours and add them to the queue if necessary. This algorithm only needs to check the neighbours of each foreground pixel once and doesn't check the neighbours of background pixels. The pseudocode is: algorithm OneComponentAtATime(data) input : imageData[xDim][yDim] initialization : label = 0, labelArray[xDim][yDim] = 0, statusArray[xDim][yDim] = false, queue1, queue2; for i = 0 to xDim do for j = 0 to yDim do if imageData[i][j] has not been processed do if imageData[i][j] is a foreground pixel do check its four neighbors(north, south, east, west) : if neighbor is not processed do if neighbor is a foreground pixel do add it to queue1 else update its status to processed end if labelArray[i][j] = label (give label) statusArray[i][j] = true (update status) while queue1 is not empty do For each pixel in the queue do : check its four neighbors if neighbor is not processed do if neighbor is a foreground pixel do add it to queue2 else update its status to processed end if give it the current label update its status to processed remove the current element from queue1 copy queue2 into queue1 end While increase the label end if else update its status to processed end if end if end if end for end for === Two-pass === Relatively simple to implement and understand, the two-pass algorithm, (also known as the Hoshen–Kopelman algorithm) iterates through 2-dimensional binary data. The algorithm makes two passes over the image: the first pass to assign temporary labels and record equivalences, and the second pass to replace each temporary label by the smallest label of its equivalence class. The input data can be modified in situ (which carries the risk of data corruption), or labeling information can be maintained in an additional data structure. Connectivity checks are carried out by checking neighbor pixels' labels (neighbor elements whose labels are not assigned yet are ignored), or say, the north-east, the north, the north-west and the west of the current pixel (assuming 8-connectivity). 4-connectivity uses only north and west neighbors of the current pixel. The following conditions are checked to determine the value of the label to be assigned to the current pixel (4-connectivity is assumed) Conditions to check: Does the pixel to the left (west) have the same value as the current pixel? Yes – We are in the same region. Assign the same label to the current pixel No – Check next condition Do both pixels to the north and west of the current pixel have the same value as the current pixel but not the same label? Yes – We know that the north and west pixels belong to the same region and must be merged. Assign the current pixel the minimum of the north and west labels, and record their equivalence relationship No – Check next condition Does the pixel to the left (west) have a different value and the one to the north the same value as the current pixel? Yes – Assign the label of the north pixel to the current pixel No – Check next condition Do the pixel's north and west neighbors have different pixel values than current pixel? Yes – Create a new label id and assign it to the current pixel The algorithm continues this way, and creates new region labels whenever necessary. The key to a fast algorithm, however, is how this merging is done. This algorithm uses the union-find data structure which provides excellent performance for keeping track of equivalence relationships. Union-find essentially stores labels which correspond to the same blob in a disjoint-set data structure, making it easy to remember the equivalence of two labels by the use of an interface method E.g.: findSet(l). findSet(l) returns the minimum label value that is equivalent to the function argument 'l'. Once the initial labeling and equivalence recording is completed, the second pass merely replaces each pixel label with its equivalent disjoint-set representative element. A faster-scanning algorithm for connected-region extraction is presented below. On the first pass: Iterate through each element of the data by column, then by row (Raster Scanning) If the element is not the background Get the neighboring elements of the current element If there are no neighbors, uniquely
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How to Choose an AI Text-to-video Tool

Comparing the best AI text-to-video tool? An AI text-to-video tool is software that uses machine learning to help you get more done — it lowers the barrier so anyone can produce professional output. Privacy matters too: check whether your data trains the model and whether a no-log or enterprise tier is available. Whether you are a beginner or a pro, the right AI text-to-video tool slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.
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HOCR

hOCR is an open standard of data representation for formatted text obtained from optical character recognition (OCR). The definition encodes text, style, layout information, recognition confidence metrics and other information using Extensible Markup Language (XML) in the form of Hypertext Markup Language (HTML) or XHTML. == Software == The following OCR software can output the recognition result as hOCR file: OCRopus Tesseract Cuneiform ghostscript HebOCR gcv2hocr gImageReader == Example == The following example is an extract of an hOCR file: The recognized text is stored in normal text nodes of the HTML file. The distribution into separate lines and words is here given by the surrounding span tags. Moreover, the usual HTML entities are used, for example the p tag for a paragraph. Additional information is given in the properties such as: different layout elements such as "ocr_par", "ocr_line", "ocrx_word" geometric information for each element with a bounding box "bbox" language information "lang" some confidence values "x_wconf" == bbox == === General === The Layout of the Bounding Box Object or bbox Object is Grammar. property-name = "bbox" property-value = uint uint uint uint ==== Example ==== bbox 0 0 100 200 The bbox - short for "bounding box" - of an element is a rectangular box around this element, which is defined by the upper-left corner (x0, y0) and the lower-right corner (x1, y1). the values are with reference to the top-left corner of the document image and measured in pixels the order of the values are x0 y0 x1 y1 = "left top right bottom" ===== Usage ===== Use x_bboxes below for character bounding boxes. Do not use bbox unless the bounding box of the layout component is, in fact, rectangular, some non-rectangular layout components may have rectangular bounding boxes if the non-rectangularity is caused by floating elements around which text flows. The bounding box bbox of this line is shown in blue and it is span by the upper-left corner (10, 20) and the lower-right corner (160, 30). All coordinates are measured with reference to the top-left corner of the document image which border is drawn in black. == Searchable PDF files == The hOCR format is most commonly used in order to make searchable PDF files or as an extracted metadata of the PDF file. In order to create searchable PDF files we can use a scanned document image and a .hocr file of the particular image. We can use the following open source tools in order to achieve that. === hocr-tools === Source: hocr-tools is an open source library written in Python. It has a command-line utility attached in the scripts called hocr-pdf that enables us to convert standard hocr files to a searchable PDF file. It is also worth noting that the version for dealing with hocr files in RTL or non-Latin scripts like Arabic, we need to use the GitHub repository at the moment. hocr-pdf We can use the hocr-pdf utility using the following basic syntax. hocr-pdf—savefile final.pdf folder_images_and_hocr The folder_images_and_hocr must contain the respective .jpg and .hocr format files with their file extensions changed. ==== Known issues ==== Some of the known issues of hocr-pdf script in PyPI installation are the following. Not up to date with GitHub repository. hocr-pdf is broken on line 134 due to decodebytes() depreciated after Python 3.1 ==== Known fixes ==== Compile hocr-tools using latest GitHub repository. === hocr2pdf === hocr2pdf is another library that supports the conversion of hocr files. It is written in C++ and is cross-compatible with other libraries. It also has support for UTF-8 languages but that may require some additional debugging and browsing through some google conversation records to achieve that. According to Ubuntu Manpages,ExactImage is a fast C++ image processing library. Unlike many other library frameworks it allows operation in several color spaces and bit depths natively, resulting in low memory and computational requirements. hocr2pdf creates well layouted, searchable PDF files from hOCR (annotated HTML) input obtained from an OCR system. == hOCR to PDF attempts == In addition to the following discussed and stable libraries there have been many contributions to the hOCR format over the years with support from many of the early adopters of this format. You can get access to inlaying text on an Image with hOCR and converting that in a PDF file using Python 2 with this 12-year-old script as of 2021. This script can also be updated and made functional by converting that Python 2 Source code to Python 3 Supported Context. - HOCRConverter by jbrinley (Documentation) === HOCRConverter === The HOCRConverter is a script written in Python 2.x that can used in order to convert a hOCR file with a specified image file in order to convert it to a searchable PDF file. You can see the documentation using the link above. ==== Known issues ==== Has not been tested. Does not natively support Python 3.x
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European Association for Machine Translation

The European Association for Machine Translation is the European branch of the International Association for Machine Translation Archived 2010-06-24 at the Wayback Machine. It is a non-profit organisation and organises conferences and workshops on the subject of machine translation. It was registered in 1991 in Switzerland and is the only organisation of its type in Europe.
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INaturalist

iNaturalist is an American 501(c)(3) nonprofit social network of naturalists, citizen scientists, and biologists built on the concept of mapping and sharing observations of biodiversity across the globe. iNaturalist may be accessed via its website or from its mobile applications. iNaturalist includes an automated species identification tool, and users further assist each other in identifying organisms from photographs and sound recordings. As of 5 August 2025, iNaturalist users had contributed nearly 300 million observations of plants, animals, fungi, and other organisms worldwide, and 400,000 users were active in the previous 30 days. iNaturalist serves as an important resource of open data for biodiversity research, conservation, and education, describing itself as "an online social network of people sharing biodiversity information to help each other learn about nature." It is the primary application for crowd-sourced biodiversity data in places such as Mexico, southern Africa, and Australia, and the project has been called "a standard-bearer for natural history mobile applications." Most of iNaturalist's software is open source. It has contributed to over 4,000 research papers and is widely used by scientists, land managers, and conservationists worldwide. The platform has also been active in the discovery of new species and rediscovery of species previously assumed to be extinct. == History == iNaturalist began in 2008 as a UC Berkeley School of Information Master's final project of Nate Agrin, Jessica Kline, and Ken-ichi Ueda. Agrin and Ueda continued work on the site with Sean McGregor, a web developer. In 2011, Ueda began collaboration with Scott Loarie, a research fellow at Stanford University and lecturer at UC Berkeley. Ueda and Loarie are the current co-directors of iNaturalist.org. The organization merged with the California Academy of Sciences on 24 April 2014. In 2017, iNaturalist became a joint initiative between the California Academy of Sciences and the National Geographic Society. With these collaborations and growing popularity of the site since 2012, the number of participants and observations has roughly doubled each year. In 2014, iNaturalist reached 1 million observations. Later, as of October 2023, there were 181 million observations (163 million verifiable). On 11 July 2023 iNaturalist announced its status as a newly independent 501(c)(3) nonprofit organization. === Google AI controversy === On 9 June 2025 Google announced that iNaturalist would be part of its "Generative AI Accelerator". This announcement, paired with the initial lack of information on the iNaturalist site, led to outcry from many iNaturalist users in the blog comments and forum, worrying about the consequences for the environment, volunteer engagement, reliability and raised questions about the decision making within iNaturalist, while some saw the backlash as a sign that people want to resist 'corrosive technologies'. PZ Myers, a biology professor who uses iNaturalist in his teaching, published an article on his website Pharyngula stating that "any decision that drives people away and replaces them with a hallucinating bot is a bad decision". == Platforms == Users can interact with iNaturalist in the following ways: through the iNaturalist.org website, through two mobile apps: iNaturalist (iOS/Android) and Seek by iNaturalist (iOS/Android), or through partner organizations such as the Global Biodiversity Information Facility (GBIF) website. On the iNaturalist.org website, visitors can search the public dataset and interact with other people adding observations and identifications. The website provides tools for registered users to add, identify, and discuss observations, write journal posts, explore information about species, create project pages to recruit participation, and coordinate work on their topics of interest. On the iNaturalist mobile app, users can create and share nature observations to the online dataset, explore observations both nearby and around the world, and learn about different species. Seek by iNaturalist, a separate app marketed to families, requires no online account registration and all observations may remain private. Seek incorporates features of gamification, such as providing a list of nearby organisms to find and encouraging the collection of badges and participation in challenges. Seek was initially released in the spring of 2018. == Observations == The iNaturalist platform is based on crowdsourcing of observations and identifications. An iNaturalist observation records a person's encounter with an individual organism at a particular time and place. An iNaturalist observation may also record evidence of an organism, such as animal tracks, nests, or scat. The scope of iNaturalist excludes natural but inert subjects such as geologic or hydrologic features. Users typically upload photos as evidence of their findings, though audio recordings are also accepted, and such evidence is not a strict requirement. Users may share observation locations publicly, "obscure" them to display a less precise location or make the locations completely private. iNaturalist users can add identifications to each other's observations in order to confirm or improve the identification of the observation. Observations are classified as "Casual", "Needs ID" (needs identification), or "Research Grade" based on the quality of the data provided and the community identification process. Any quality of data can be downloaded from iNaturalist and "Research Grade" observations are often incorporated into other online databases such as the Global Biodiversity Information Facility and the Atlas of Living Australia. === Automated species identification === In addition to observations being identified by others in the community, iNaturalist includes an automated species identification tool, first released in 2017. Images can be identified via a computer vision model which has been trained on the large database of the observations on iNaturalist. Multiple species suggestions are typically provided with the suggestion that the software guesses to be most likely is at the top of the list. A broader taxon such as a genus or family is commonly provided if the model is unsure of the species. It is trained once or twice a year, and the threshold for species included in the training set has changed over time. It can be difficult for the model to guess correctly if the species in question is infrequently observed or hard to identify from images alone, or if the image submitted has poor lighting, is blurry, or contains multiple subjects. In February 2023, iNaturalist released v2.1 of its computer vision model, which was trained on a new source model which performed significantly better than the previous models trained using a different source model. In April 2025 iNaturalist released an updated app for iOS, changing the original version to "iNaturalist Classic." == Projects == Users have created and contributed to tens of thousands of different projects on iNaturalist. The platform is commonly used to record observations during bioblitzes, which are biological surveying events that attempt to record all the species that occur within a designated area, and a specific project type on iNaturalist. Other project types include collections of observations by location or taxon or documenting specific types of observations such as animal tracks and signs, the spread of invasive species, roadkill, fishing catches, or discovering new species. In 2011, iNaturalist was used as a platform to power the Global Amphibian and Global Reptile BioBlitzes, in which observations were used to help monitor the occurrence and distribution of the world's reptiles and amphibian species. The US National Park Service partnered with iNaturalist to record observations from the 2016 National Parks BioBlitz. That project exceeded 100,000 observations in August 2016. In 2017, the United Nations Environment Programme teamed up with iNaturalist to celebrate World Environment Day.. In 2022, Reef Ecologic teamed up with iNaturalist to celebrate World Oceans Day. === City Nature Challenge === In 2016, Lila Higgins from the Natural History Museum of Los Angeles County and Alison Young from the California Academy of Sciences co-founded the City Nature Challenge (CNC). In the first City Nature Challenge, naturalists in Los Angeles and the San Francisco Bay Area documented over 20,000 observations with the iNaturalist platform. In 2017, the CNC expanded to 16 cities across the United States and collected over 125,000 observations of wildlife in 5 days. The CNC expanded to a global audience in 2018, with 68 cities participating from 19 countries, with some cities using community science platforms other than iNaturalist to participate. In 4 days, over 17,000 people cataloged over 440,000 nature observations in urban regions around the world. In 2019, the CNC once again expanded, with 35,000 parti
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IBM optical mark and character readers

IBM designed, manufactured and sold optical mark and character readers from 1960 until 1984. The IBM 1287 is notable as being the first commercially sold scanner capable of reading handwritten numbers. == Initial development work == IBM Poughkeepsie studied machine character recognition from 1950 till 1954, developing an experimental machine that used a cathode-ray-tube attached an IBM 701 which performed the character analysis. They pursued a technique known as lakes and bays which examined different areas of dark and light where the lakes were white areas enclosed by black and the bays were partially enclosed areas. Their machine and mission was moved to IBM Endicott in 1954, where research continued. From 1955 to 1956 they then worked on the VIDOR (Visual Document Reader) program, but they could not get agreement on acceptable reject rate. The developers felt 80% recognition was acceptable (meaning 20% of documents would need to be manually processed), while product planners and IBM Marketing felt that compared to punched card, the reject rate was unacceptably high. This led to no new products being released. In 1956 the American Bankers Association chose to use Magnetic Ink Character Recognition (MICR) to automate check handling, rejecting a proposed solution generated by an IBM Poughkeepsie banking project that used optical characters formed by vertical bars and digits. IBM developed a magnetic read head to handle the new standard, releasing the IBM 1210 MICR reader/sorter in 1959. The development work for this product both with read heads and document handling, helped move optical character recognition forward, with development focusing on reading one or two lines of print from a paper document larger than an IBM punched card. The first product to be released was the IBM 1418. == IBM 123x Optical Mark Readers == The IBM 1230, IBM 1231, and IBM 1232 were optical mark readers used to input the contents of data sources such as questionnaires, test results, surveys as well as historical data that could be easily entered as marks on sheets. Educational institutes used them to score test results and they were effectively a replacement for the IBM 805 Test Scoring Machine that used electrical resistance and a mark sense pencil to score a test, rather than optical mark detection. They were developed and manufactured by IBM Rochester. They have the following features: A pneumatic input hopper that can hold approximately 600 sheets Two output stackers: the normal stacker that holds 600 sheets and the select (or reject) stacker which holds 50 sheets. Pluggable SMS printed circuit cards They can read positional marks made by a lead pencil using an optical read head that consists of photovoltaic(solar) cells and lamps The 1230 has 21 photovoltaic cells, 20 for reading the pencil marks and one to read timing marks on the right hand border of the sheet. The 1231 and 1232 have 22 photovoltaic cells, 20 to read data, one to read timing marks and one to read a special feature called a master mark. Input size is a 8+1⁄2 in × 11 in (22 cm × 28 cm) sheet called a data sheet that can have up to 1000 marked or printed positions per side. Uses electromechanical devices known as sonic delay lines to store results. === IBM 1230 Optical Mark Scoring Reader === The IBM 1230 is an offline optical mark scoring machine announced on 2 November 1962 that was designed to read and scores 1,200 answer sheets per hour. Scored results are printed via a wire matrix printer on the right margin of each answer sheet as it is processed. Two master sheets are required for the process: one that encoded the correct answers and one for the machine to record run information. Output could be sent to an IBM 534 Model 3 Card Punch as an option, which limits throughput to 750 sheets per hour when punching 80 columns of data. === IBM 1231 Optical Mark Page Reader === The IBM 1231 is an online optical mark reader that was designed to read and score 2000 test answer sheets per hour, depending on downstream operations. The correct answers for the test can either be entered using a master sheet (like the 1230) or sent to the 1231 using the optional master-mark special feature. === IBM 1232 Optical Mark Page Reader === The IBM 1232 is an offline optical mark reader that was designed to read up to 2000 marked sheets per hour. Documents can be read at up to 2000 sheets per hour, but this depends on the number of characters that need to be punched from each sheet. The IBM 1232 reads the marks and then punches them into cards using a IBM 534 Model 3 Card Punch. Together they can read up to 64,000 characters per hour or 800 fully punched cards. === Example customers === The California Test Bureau (CTB) that provided standardised achievement tests for educational institutes across the USA, began replacing their IBM 805s with IBM 1230s in 1963. They then installed two IBM 1232s in 1964. Being able to use a full 8+1⁄2 in × 11 in (22 cm × 28 cm) answer sheet rather than a 7+3⁄8 in × 3+1⁄4 in (18.7 cm × 8.3 cm) mark sense card, eliminated the need to use multiple answer cards per test per student, as well as dramatically increased the marking speed for test answers. Credit Bureau Services of Dallas used an IBM 1232 in 1966 as part of their first computerisation project. They marked credit history data onto optical scanning sheets that were fed into their IBM 1232. The attached IBM 534 then punched this data onto punched cards, which were then fed into their IBM System/360 Model 30. In 1968 the US Army Corps of Engineers Coastal Engineering Research Center (CERC) began using special log books for their coastal surveyors to record coastal survey data, which was then converted to punched cards by an IBM 1232. == IBM 2956 Optical Mark/Hole Reader == The IBM 2956 Models 2 and 3 are custom build optical mark/hole readers designed to be attached to an IBM 2740 Communications Terminal. The IBM 2956-2 can read cards that have either been hand or machine marked or that have been punched. The cards can be fed by hand or from the 400 card hopper. It has a 400 card stacker. The 2956-2 could be ordered by request for price quotation (RPQ) 843086. The IBM 2956-3 can read cards that have either been hand or machine marked or that have been punched. It can also read marked sheets up to 9 in × 14 in (230 mm × 360 mm) in size, although only a 3+1⁄4 in (83 mm) band along the side of the sheet can be read (the width of a punched card). It does not have a hopper or a stacker, so each card or sheet must be manually fed into the machine. The 2956-3 could be ordered by request for price quotation (RPQ) 843106. The 2956-3 could be attached to an IBM 3276 or IBM 3278 display station with RPQ UB9001. One use case for the IBM 2956 is to grade school tests. On completion of a learning module a student can use an optical scan-type card to record answers to up to 27 questions, with up to 5 choices per question. They are scanned by the reader and the results are then transmitted to an IBM System/360 in remote job entry mode and can also be printed on the IBM 2740. The reader can also be attached to an IBM 3735 which transmits results to an IBM System/370 and which prints results on an IBM 3286 printer. They can also be attached to an IBM System/3. Note that the IBM 2956 Model 5 (2956-5) was a banking reader/sorter. == IBM 1282 Optical Reader Card Punch == The IBM 1282 is an offline optical reader that is used to read embossed credit card receipts, a mark read field or machine printed characters in three different fonts. It then outputs this data onto a punched card. It was developed and manufactured by IBM Endicott. It proved popular and within two years of announcement 100 machines were installed or on order. === Example customer === The New York Department of Motor Vehicles reported that from 1964 until 1968 they were using an IBM 1282 to read machine printed license renewal slips that had been mailed back as part of the renewal process. They would scan the slip and then process the resulting punched card. This worked well until the DMV decided to request renewals include the drivers Social Security Number (SSN), which meant a handwritten number needed to be either manually keyed or a new scanning device procured. They switched to the IBM 1287 in 1968. == IBM 1285 Optical Reader == The IBM 1285 is an online optical reader that is used to read printed paper tapes from cash registers or adding machines. It was developed by IBM Endicott and manufactured by IBM Rochester. The IBM 1285 attaches to an IBM 1401, 1440, 1460 or System/360. It has a small round screen to display characters being read and it has a keyboard to enter header information and to optionally enter character corrections for rejected characters. It can read a 200 ft (61 m) roll or paper tape in three-and-a half minutes, reading data at speeds of up to 3000 lines per minute. It can mark the tape with a dot to indicate unreadable characters, so they can be r
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The Best Free AI Essay Writer for Beginners

In search of the best AI essay writer? An AI essay writer is software that uses machine learning to help you get more done — it turns a rough idea into a polished result in seconds. When choosing one, weigh output quality, pricing, export formats, and how well it fits the tools you already use. Whether you are a beginner or a pro, the right AI essay writer slots into your workflow and pays for itself fast. We tested the leading options and ranked them by quality, value, and ease of use.
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Collocation

In corpus linguistics, a collocation is a series of words or terms that co-occur more often than would be expected by chance. In phraseology, a collocation is a type of compositional phraseme, meaning that it can be understood from the words that make it up. This contrasts with an idiom, where the meaning of the whole cannot be inferred from its parts, and may be completely unrelated. There are about seven main types of collocations: adjective + noun, noun + noun (such as collective nouns), noun + verb, verb + noun, adverb + adjective, verbs + prepositional phrase (phrasal verbs), and verb + adverb. Collocation extraction is a computational technique that finds collocations in a document or corpus, using various computational linguistics elements resembling data mining. == Expanded definition == Collocations are partly or fully fixed expressions that become established through repeated context-dependent use. Such terms as crystal clear, middle management, nuclear family, and cosmetic surgery are examples of collocated pairs of words. Collocations can be in a syntactic relation (such as verb–object: make and decision), lexical relation (such as antonymy), or they can be in no linguistically defined relation. Knowledge of collocations is vital for the competent use of a language: a grammatically correct sentence will stand out as awkward if collocational preferences are violated. This makes collocation a common focus for language teaching. Corpus linguists specify a key word in context (KWIC) and identify the words immediately surrounding them, to illustrate the way words are used in practice. The processing of collocations involves a number of parameters, the most important of which is the measure of association, which evaluates whether the co-occurrence is purely by chance or statistically significant. Due to the non-random nature of language, most collocations are classed as significant, and the association scores are simply used to rank the results. Commonly used measures of association include mutual information, t scores, and log-likelihood. Rather than select a single definition, Gledhill proposes that collocation involves at least three different perspectives: co-occurrence, a statistical view, which sees collocation as the recurrent appearance in a text of a node and its collocates; construction, which sees collocation either as a correlation between a lexeme and a lexical-grammatical pattern, or as a relation between a base and its collocative partners; and expression, a pragmatic view of collocation as a conventional unit of expression, regardless of form. These different perspectives contrast with the usual way of presenting collocation in phraseological studies. Traditionally speaking, collocation is explained in terms of all three perspectives at once, in a continuum: == In dictionaries == In 1933, Harold Palmer's Second Interim Report on English Collocations highlighted the importance of collocation as a key to producing natural-sounding language, for anyone learning a foreign language. Thus from the 1940s onwards, information about recurrent word combinations became a standard feature of monolingual learner's dictionaries. As these dictionaries became "less word-centred and more phrase-centred", more attention was paid to collocation. This trend was supported, from the beginning of the 21st century, by the availability of large text corpora and intelligent corpus-querying software, making it possible to provide a more systematic account of collocation in dictionaries. Using these tools, dictionaries such as the Macmillan English Dictionary and the Longman Dictionary of Contemporary English included boxes or panels with lists of frequent collocations. There are also a number of specialized dictionaries devoted to describing the frequent collocations in a language. These include (for Spanish) Redes: Diccionario combinatorio del español contemporaneo (2004), (for French) Le Robert: Dictionnaire des combinaisons de mots (2007), and (for English) the LTP Dictionary of Selected Collocations (1997) and the Macmillan Collocations Dictionary (2010). == Statistically significant collocation == Student's t-test can be used to determine whether the occurrence of a collocation in a corpus is statistically significant. For a bigram w 1 w 2 {\displaystyle w_{1}w_{2}} , let P ( w 1 ) = # w 1 N {\displaystyle P(w_{1})={\frac {\#w_{1}}{N}}} be the unconditional probability of occurrence of w 1 {\displaystyle w_{1}} in a corpus with size N {\displaystyle N} , and let P ( w 2 ) = # w 2 N {\displaystyle P(w_{2})={\frac {\#w_{2}}{N}}} be the unconditional probability of occurrence of w 2 {\displaystyle w_{2}} in the corpus. The t-score for the bigram w 1 w 2 {\displaystyle w_{1}w_{2}} is calculated as: where x ¯ = # w i w j N {\displaystyle {\bar {x}}={\frac {\#w_{i}w_{j}}{N}}} is the sample mean of the occurrence of w 1 w 2 {\displaystyle w_{1}w_{2}} , # w 1 w 2 {\displaystyle \#w_{1}w_{2}} is the number of occurrences of w 1 w 2 {\displaystyle w_{1}w_{2}} , μ = P ( w i ) P ( w j ) {\displaystyle \mu =P(w_{i})P(w_{j})} is the probability of w 1 w 2 {\displaystyle w_{1}w_{2}} under the null-hypothesis that w 1 {\displaystyle w_{1}} and w 2 {\displaystyle w_{2}} appear independently in the text, and s 2 = x ¯ ( 1 − x ¯ ) ≈ x ¯ {\displaystyle s^{2}={\bar {x}}(1-{\bar {x}})\approx {\bar {x}}} is the sample variance. With a large N {\displaystyle N} , the t-test is equivalent to a Z-test.
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Query understanding

Query understanding is the process of inferring the intent of a search engine user by extracting semantic meaning from the searcher’s keywords. Query understanding methods generally take place before the search engine retrieves and ranks results. It is related to natural language processing but specifically focused on the understanding of search queries. == Methods == === Stemming and lemmatization === Many languages inflect words to reflect their role in the utterance they appear in. The variation between various forms of a word is likely to be of little importance for the relatively coarse-grained model of meaning involved in a retrieval system, and for this reason the task of conflating the various forms of a word is a potentially useful technique to increase recall of a retrieval system. Stemming algorithms, also known as stemmers, typically use a collection of simple rules to remove suffixes intended to model the language’s inflection rules. For some languages, there are simple lemmatisation methods to reduce a word in query to its lemma or root form or its stem; for others, this operation involves non-trivial string processing and may require recognizing the word's part of speech or referencing a lexical database. The effectiveness of stemming and lemmatization varies across languages. === Query Segmentation === Query segmentation is a key component of query understanding, aiming to divide a query into meaningful segments. Traditional approaches, such as the bag-of-words model, treat individual words as independent units, which can limit interpretative accuracy. For languages like Chinese, where words are not separated by spaces, segmentation is essential, as individual characters often lack standalone meaning. Even in English, the BOW model may not capture the full meaning, as certain phrases—such as "New York"—carry significance as a whole rather than as isolated terms. By identifying phrases or entities within queries, query segmentation enhances interpretation, enabling search engines to apply proximity and ordering constraints, ultimately improving search accuracy and user satisfaction. === Entity recognition === Entity recognition is the process of locating and classifying entities within a text string. Named-entity recognition specifically focuses on named entities, such as names of people, places, and organizations. In addition, entity recognition includes identifying concepts in queries that may be represented by multi-word phrases. Entity recognition systems typically use grammar-based linguistic techniques or statistical machine learning models. === Query rewriting === Query rewriting is the process of automatically reformulating a search query to more accurately capture its intent. Query expansion adds additional query terms, such as synonyms, in order to retrieve more documents and thereby increase recall. Query relaxation removes query terms to reduce the requirements for a document to match the query, thereby also increasing recall. Other forms of query rewriting, such as automatically converting consecutive query terms into phrases and restricting query terms to specific fields, aim to increase precision. === Spelling Correction === Automatic spelling correction is a critical feature of modern search engines, designed to address common spelling errors in user queries. Such errors are especially frequent as users often search for unfamiliar topics. By correcting misspelled queries, search engines enhance their understanding of user intent, thereby improving the relevance and quality of search results and overall user experience.
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Top 10 AI Presentation Makers Compared (2026)

Trying to pick the best AI presentation maker? An AI presentation maker is software that uses machine learning to help you get more done — it scales effortlessly from a single task to thousands. The best picks balance beginner-friendly simplicity with the depth power users need, and they ship updates often. Whether you are a beginner or a pro, the right AI presentation maker slots into your workflow and pays for itself fast. Read on for hands-on impressions, pricing tiers, and the standout features that matter.
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Dynamic topic model

Within statistics, Dynamic topic models' are generative models that can be used to analyze the evolution of (unobserved) topics of a collection of documents over time. This family of models was proposed by David Blei and John Lafferty and is an extension to Latent Dirichlet Allocation (LDA) that can handle sequential documents. In LDA, both the order the words appear in a document and the order the documents appear in the corpus are oblivious to the model. Whereas words are still assumed to be exchangeable, in a dynamic topic model the order of the documents plays a fundamental role. More precisely, the documents are grouped by time slice (e.g.: years) and it is assumed that the documents of each group come from a set of topics that evolved from the set of the previous slice. == Topics == Similarly to LDA and pLSA, in a dynamic topic model, each document is viewed as a mixture of unobserved topics. Furthermore, each topic defines a multinomial distribution over a set of terms. Thus, for each word of each document, a topic is drawn from the mixture and a term is subsequently drawn from the multinomial distribution corresponding to that topic. The topics, however, evolve over time. For instance, the two most likely terms of a topic at time t could be "network" and "Zipf" (in descending order) while the most likely ones at time t+1 could be "Zipf" and "percolation" (in descending order). == Model == Define α t {\displaystyle \alpha _{t}} as the per-document topic distribution at time t. β t , k {\displaystyle \beta _{t,k}} as the word distribution of topic k at time t. η t , d {\displaystyle \eta _{t,d}} as the topic distribution for document d in time t, z t , d , n {\displaystyle z_{t,d,n}} as the topic for the nth word in document d in time t, and w t , d , n {\displaystyle w_{t,d,n}} as the specific word. In this model, the multinomial distributions α t + 1 {\displaystyle \alpha _{t+1}} and β t + 1 , k {\displaystyle \beta _{t+1,k}} are generated from α t {\displaystyle \alpha _{t}} and β t , k {\displaystyle \beta _{t,k}} , respectively. Even though multinomial distributions are usually written in terms of the mean parameters, representing them in terms of the natural parameters is better in the context of dynamic topic models. The former representation has some disadvantages due to the fact that the parameters are constrained to be non-negative and sum to one. When defining the evolution of these distributions, one would need to assure that such constraints were satisfied. Since both distributions are in the exponential family, one solution to this problem is to represent them in terms of the natural parameters, that can assume any real value and can be individually changed. Using the natural parameterization, the dynamics of the topic model are given by β t , k | β t − 1 , k ∼ N ( β t − 1 , k , σ 2 I ) {\displaystyle \beta _{t,k}|\beta _{t-1,k}\sim N(\beta _{t-1,k},\sigma ^{2}I)} and α t | α t − 1 ∼ N ( α t − 1 , δ 2 I ) {\displaystyle \alpha _{t}|\alpha _{t-1}\sim N(\alpha _{t-1},\delta ^{2}I)} . The generative process at time slice 't' is therefore: Draw topics β t , k | β t − 1 , k ∼ N ( β t − 1 , k , σ 2 I ) ∀ k {\displaystyle \beta _{t,k}|\beta _{t-1,k}\sim N(\beta _{t-1,k},\sigma ^{2}I)\forall k} Draw mixture model α t | α t − 1 ∼ N ( α t − 1 , δ 2 I ) {\displaystyle \alpha _{t}|\alpha _{t-1}\sim N(\alpha _{t-1},\delta ^{2}I)} For each document: Draw η t , d ∼ N ( α t , a 2 I ) {\displaystyle \eta _{t,d}\sim N(\alpha _{t},a^{2}I)} For each word: Draw topic Z t , d , n ∼ Mult ( π ( η t , d ) ) {\displaystyle Z_{t,d,n}\sim {\textrm {Mult}}(\pi (\eta _{t,d}))} Draw word W t , d , n ∼ Mult ( π ( β t , Z t , d , n ) ) {\displaystyle W_{t,d,n}\sim {\textrm {Mult}}(\pi (\beta _{t,Z_{t,d,n}}))} where π ( x ) {\displaystyle \pi (x)} is a mapping from the natural parameterization x to the mean parameterization, namely π ( x i ) = exp ⁡ ( x i ) ∑ i exp ⁡ ( x i ) {\displaystyle \pi (x_{i})={\frac {\exp(x_{i})}{\sum _{i}\exp(x_{i})}}} . == Inference == In the dynamic topic model, only W t , d , n {\displaystyle W_{t,d,n}} is observable. Learning the other parameters constitutes an inference problem. Blei and Lafferty argue that applying Gibbs sampling to do inference in this model is more difficult than in static models, due to the nonconjugacy of the Gaussian and multinomial distributions. They propose the use of variational methods, in particular, the Variational Kalman Filtering and the Variational Wavelet Regression. == Applications == In the original paper, a dynamic topic model is applied to the corpus of Science articles published between 1881 and 1999 aiming to show that this method can be used to analyze the trends of word usage inside topics. The authors also show that the model trained with past documents is able to fit documents of an incoming year better than LDA. A continuous dynamic topic model was developed by Wang et al. and applied to predict the timestamp of documents. Going beyond text documents, dynamic topic models were used to study musical influence, by learning musical topics and how they evolve in recent history.
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Leslie P. Kaelbling

Leslie Pack Kaelbling is an American roboticist and the Panasonic Professor of Computer Science and Engineering at the Massachusetts Institute of Technology. She is widely recognized for adapting partially observable Markov decision processes from operations research for application in artificial intelligence and robotics. Kaelbling received the IJCAI Computers and Thought Award in 1997 for applying reinforcement learning to embedded control systems and developing programming tools for robot navigation. In 2000, she was elected as a Fellow of the Association for the Advancement of Artificial Intelligence. == Career == Kaelbling received an A. B. in Philosophy in 1983 and a Ph.D. in Computer Science in 1990, both from Stanford University. During this time she was also affiliated with the Center for the Study of Language and Information. She then worked at SRI International and the affiliated robotics spin-off Teleos Research before joining the faculty at Brown University. She left Brown in 1999 to join the faculty at MIT. Her research focuses on decision-making under uncertainty, machine learning, and sensing with applications to robotics. == Journal of Machine Learning Research == In the spring of 2000, she and two-thirds of the editorial board of the Kluwer-owned journal Machine Learning resigned in protest to its pay-to-access archives with simultaneously limited financial compensation for authors. Kaelbling co-founded and served as the first editor-in-chief of the Journal of Machine Learning Research, a peer-reviewed open access journal on the same topics which allows researchers to publish articles for free and retain copyright with its archives freely available online. In response to the mass resignation, Kluwer changed their publishing policy to allow authors to self-archive their papers online after peer-review. Kaelbling responded that this policy was reasonable and would have made the creation of an alternative journal unnecessary, but the editorial board members had made it clear they wanted such a policy and it was only after the threat of resignations and the actual founding of JMLR that the publishing policy finally changed. == Selected works == Reinforcement Learning: A Survey (LP Kaelbling, ML Littman, AW Moore). Journal of Artificial Intelligence Research (JAIR) 4 (1996) 237-285. A highly cited survey on the field of reinforcement learning. Planning and acting in partially observable stochastic domains (LP Kaelbling, ML Littman, AR Cassandra). Artificial Intelligence 101 (1), 99-134. Acting under uncertainty: Discrete Bayesian models for mobile-robot navigation (AR Cassandra, LP Kaelbling, JA Kurien). Intelligent Robots and Systems (2) 963-972. The synthesis of digital machines with provable epistemic properties (SJ Rosenschein, LP Kaelbling). Proceedings of the 1986 Conference on Theoretical Aspects of Reasoning about Knowledge, 83-98. Practical reinforcement learning in continuous spaces (WD Smart, LP Kaelbling). 2000 International Conference on Machine Learning (ICML), 903-910. Hierarchical task and motion planning in the now (LP Kaelbling, T Lozano-Pérez). 2011 IEEE International Conference on Robotics and Automation (ICRA), 1470-1477.
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DigitaltMuseum

DigitaltMuseum (lit. 'The Digital Museum') is a website database in Norwegian and Swedish for art, images and cultural history museums. The service was established in 2009 after a trial period. The database is developed and operated by KulturIT. KulturIT ANS was established by the Norwegian Museum of Cultural History and Maihaugen in consultation with the Norwegian Archive, Library and Museum Authority (ABM) in 2007. In 2015, the company underwent a corporate transformation and KulturIT AS was established on 12 February. The website has per 2025 around 2,548,022 images. Many of the images are in the public domain or under Creative Commons licenses and are being imported into Wikimedia Commons. The website's API was developed in 2012. == Institutions == As of 2025, there are 223 collaborating museums. == Mission == DigitaltMuseum aims to make the museums' collections accessible to all interested parties, regardless of time and place. The website aims to facilitate easy use of the collections through various methods including image searches, research, teaching and joint knowledge development. DigitaltMuseum contains collections from several hundred Norwegian and Swedish museums, totalling around five million objects. The website contains both historical images from the areas and themes covered by the museums, as well as images of artefacts from the collections. Parts of the collection have previously only been shown in the museums' exhibitions and books and have therefore rarely or never been shown to the public.
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Markovian discrimination

Markovian discrimination is a class of spam filtering methods used in CRM114 and other spam filters to filter based on statistical patterns of transition probabilities between words or other lexical tokens in spam messages that would not be captured using simple bag-of-words naive Bayes spam filtering. == Markovian Discrimination vs. Bag-of-Words Discrimination == A bag-of-words model contains only a dictionary of legal words and their relative probabilities in spam and genuine messages. A Markovian model additionally includes the relative transition probabilities between words in spam and in genuine messages, where the relative transition probability is the likelihood that a given word will be written next, based on what the current word is. Put another way, a bag-of-words filter discriminates based on relative probabilities of single words alone regardless of phrase structure, while a Markovian word-based filter discriminates based on relative probabilities of either pairs of words, or, more commonly, short sequences of words. This allows the Markovian filter greater sensitivity to phrase structure. Neither naive Bayes nor Markovian filters are limited to the word level for tokenizing messages. They may also process letters, partial words, or phrases as tokens. In such cases, specific bag-of-words methods would correspond to general bag-of-tokens methods. Modelers can parameterize Markovian spam filters based on the relative probabilities of any such tokens' transitions appearing in spam or in legitimate messages. == Visible and Hidden Markov Models == There are two primary classes of Markov models, visible Markov models and hidden Markov models, which differ in whether the Markov chain generating token sequences is assumed to have its states fully determined by each generated token (the visible Markov models) or might also have additional state (the hidden Markov models). With a visible Markov model, each current token is modeled as if it contains the complete information about previous tokens of the message relevant to the probability of future tokens, whereas a hidden Markov model allows for more obscure conditional relationships. Since those more obscure conditional relationships are more typical of natural language messages including both genuine messages and spam, hidden Markov models are generally preferred over visible Markov models for spam filtering. Due to storage constraints, the most commonly employed model is a specific type of hidden Markov model known as a Markov random field, typically with a 'sliding window' or clique size ranging between four and six tokens.
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AI Background Removers: Free vs Paid (2026)

Looking for the best AI background remover? An AI background remover is software that uses machine learning to help you get more done — it can save you hours every week by automating repetitive work. Most options offer a generous free tier, with paid plans unlocking higher limits, faster processing, and team features. Whether you are a beginner or a pro, the right AI background remover slots into your workflow and pays for itself fast. Read on for hands-on impressions, pricing tiers, and the standout features that matter.
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