AI Video Tools

Explore the best AI Video Tools — independent reviews, comparisons, pricing and step-by-step how-to guides, curated by Aizhi.

  • COVID-19 apps

    COVID-19 apps

    COVID-19 apps include mobile-software applications for digital contact-tracing—i.e. the process of identifying persons ("contacts") who may have been in contact with an infected individual—deployed during the COVID-19 pandemic. Numerous tracing applications have been developed or proposed, with official government support in some territories and jurisdictions. Several frameworks for building contact-tracing apps have been developed. Privacy concerns have been raised, especially about systems that are based on tracking the geographical location of app users. Less overtly intrusive alternatives include the co-option of Bluetooth signals to log a user's proximity to other cellphones. (Bluetooth technology has form in tracking cell-phones' locations.)) On 10 April 2020, Google and Apple jointly announced that they would integrate functionality to support such Bluetooth-based apps directly into their Android and iOS operating systems. India's COVID-19 tracking app Aarogya Setu became the world's fastest growing application—beating Pokémon Go—with 50 million users in the first 13 days of its release. == Rationale == Contact tracing is an important tool in infectious disease control, but as the number of cases rises time constraints make it more challenging to effectively control transmission. Digital contact tracing, especially if widely deployed, may be more effective than traditional methods of contact tracing. In a March 2020 model by the University of Oxford Big Data Institute's Christophe Fraser's team, a coronavirus outbreak in a city of one million people is halted if 80% of all smartphone users take part in a tracking system; in the model, the elderly are still expected to self-isolate en masse, but individuals who are neither symptomatic nor elderly are exempt from isolation unless they receive an alert that they are at risk of carrying the disease. Some proponents advocate for legislation exempting certain COVID-19 apps from general privacy restrictions. == Issues == === Uptake === Ross Anderson, professor of security engineering at Cambridge University, listed a number of potential practical problems with app-based systems, including false positives and the potential lack of effectiveness if takeup of the app is limited to only a small fraction of the population. In Singapore, only one person in three had downloaded the TraceTogether app by the end of June 2020, despite legal requirements for most workers; the app was also underused, as it required users to keep it open at all times on iOS. A team at the University of Oxford simulated the effect of a contact tracing app on a city of 1 million. They estimated that if the app was used in conjunction with the shielding of over-70s, then 56% of the population would have to be using the app for it to suppress the virus. This would be equivalent to 80% of smartphone users in the United Kingdom. They found that the app could still slow the spread of the virus if fewer people downloaded it, with one infection being prevented for every one or two users. In August 2020, the American Civil Liberties Union (ACLU) argued that there were disparities in smartphone use between demographics and minority groups, and that "even the most comprehensive, all-seeing contact tracing system is of little use without social and medical systems in place to help those who may have the virus — including access to medical care, testing, and support for those who are quarantined." === App store restrictions === Addressing concerns about the spread of misleading or harmful apps, Apple, Google and Amazon set limits on which types of organizations could add coronavirus-related apps to its App Store, limiting them to only "official" or otherwise reputable organizations. === Ethical principles of mass surveillance using COVID-19 contact tracing apps === The advent of COVID-19 contact tracing apps has led to concerns around privacy, the rights of app users, and governmental authority. The European Convention on Human Rights, the International Covenant on Civil and Political Rights (ICCPR) and the United Nations and the Siracusa Principles have outlined 4 principles to consider when looking at the ethical principles of mass surveillance with COVID-19 contact tracing apps. These are necessity, proportionality, scientific validity, and time boundedness. Necessity is defined as the idea that governments should only interfere with a person's rights when deemed essential for public health interests. The potential risks associated with infringements of personal privacy must be outweighed by the possibility of reducing significant harm to others. Potential benefits of contact-tracing apps that may be considered include allowing for blanket population-level quarantine measures to be lifted sooner and the minimization of people under quarantine. Hence, some contend that contact-tracing apps are justified as they may be less intrusive than blanket quarantine measures. Furthermore, the delay of an effective contact-tracing app with significant health and economic benefits may be considered unethical. Proportionality refers to the concept that a contact tracing app's potential negative impact on a person's rights should be justifiable by the severity of the health risks that are being addressed. Apps must use the most privacy-preserving options available to achieve their goals, and the selected option should not only be a logical option for achieving the goal but also an effective one. Scientific validity evaluates whether an app is effective, timely and accurate. Traditional manual contact-tracing procedures are not efficient enough for the COVID-19 pandemic, and do not consider asymptomatic transmission. Contact-tracing apps, on the other hand, can be effective COVID-19 contact-tracing tools that reduce R value to less than 1, leading to sustained epidemic suppression. However, for apps to be effective, there needs to be a minimum 56-60% uptake in the population. Apps should be continually modified to reflect current knowledge on the diseases being monitored. Some argue that contact-tracing apps should be considered societal experimental trials where results and adverse effects are evaluated according to the stringent guidelines of social experiments. Analyses should be conducted by independent research bodies and published for wide dissemination. Despite the current urgency of our pandemic situation, we should still adhere to the standard rigors of scientific evaluation. Time boundedness describe the need for establishing legal and technical sunset clauses so that they are only allowed to operate as long as necessary to address the pandemic situation. Apps should be withdrawn as soon as possible after the end of the pandemic. If the end of the pandemic cannot be predicted, the use of apps should be regularly reviewed and decisions about continued use should be made at each review. Collected data should only be retained by public health authorities for research purposes with clear stipulations on how long the data will be held for and who will be responsible for security, oversight, and ownership. === Privacy, discrimination and marginalisation concerns === The American Civil Liberties Union (ACLU) has published a set of principles for technology-assisted contact tracing and Amnesty International and over 100 other organizations issued a statement calling for limits on this kind of surveillance. The organisations declared eight conditions on governmental projects: surveillance would have to be "lawful, necessary and proportionate"; extensions of monitoring and surveillance would have to have sunset clauses; the use of data would have to be limited to COVID-19 purposes; data security and anonymity would have to be protected and shown to be protected based on evidence; digital surveillance would have to address the risk of exacerbating discrimination and marginalisation; any sharing of data with third parties would have to be defined in law; there would have to be safeguards against abuse and the rights of citizens to respond to abuses; "meaningful participation" by all "relevant stakeholders" would be required, including that of public health experts and marginalised groups. The German Chaos Computer Club (CCC) and Reporters Without Borders also issued checklists. The Exposure Notification service intends to address the problem of persistent surveillance by removing the tracing mechanism from their device operating systems once it is no longer needed. On 20 April 2020, it was reported that over 300 academics had signed a statement favouring decentralised proximity tracing applications over centralised models, given the difficulty in precluding centralised options being used "to enable unwarranted discrimination and surveillance." In a centralised model, a central database records the ID codes of meetings between users. In a decentralised model, this information is recorded on individual phones, with the role of the central

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  • Datacap

    Datacap

    Datacap (an IBM Company), a privately owned company, manufactures and sells computer software, and services. Datacap's first product, Paper Keyboard, was a "forms processing" product and shipped in 1989. In August 2010, IBM announced that it had acquired Datacap for an undisclosed amount. == Overview == Datacap sells products through a value-added distribution network worldwide. The software is classified as "enterprise software", meaning that it requires trained professionals to install and configure. Although the Company has focused on providing solutions for scanning paper documents, most recently Company materials have emphasized customer requirements to handle electronic documents ("eDocs"), documents being received into an organization electronically (usually email). Datacap claims that its software is unique because of the rules engine ("Rulerunner") used for processing inbound documents, including performing the image processing (deskew, noise removal, etc.), optical character recognition (OCR), intelligent character recognition (ICR), validations, and export-release formatting of extracted data to target ERP and line of business application.

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  • Liz Liddy

    Liz Liddy

    Elizabeth DuRoss Liddy (May 12, 1944 – August 21, 2025) was an American computer scientist and academic who was professor of information science and dean of the Syracuse University School of Information Studies. She was a pioneer in the field of natural language processing. == Early life and education == Liddy was born in Dayton, Ohio, on May 14, 1944, and grew up in Utica, New York. She was one of five children, all of whom worked in her father's family business. Liddy attended St. Francis DeSalle High School, where she was awarded a Regent's Scholarship, and eventually attended Daemen College. She was literary editor of her high school year book and edited a literary magazine during her time at college. At Daemen College Liddy studied English language and literature. After graduating Liddy remained in New York, where she volunteered in an elementary school library. She joined the Syracuse University School of Information Studies in 1983, where she started a graduate program in library science. She worked as a faculty librarian at Onondaga Community College whilst earning her degree. Here Liddy worked as a Visiting assistant professor, whilst completing her doctorate part-time in information transfer. Her dissertation research involved natural language processing, a computerized approach to analyzing text. She was hired to the faculty at Syracuse University whilst completing her PhD. == Research and career == In 1994 Liddy was the founding President of TextWise, a semantics-based search engine. The first product she developed was called Document Retrieval Using Linguistic Knowledge (DR-LINK). She left TextWise in 1999, after growing the number of employees to over 50. She started the Syracuse University Center for Natural Language Processing in 1999, and was honored with the university's Outstanding Alumni Award the following year. Liddy was appointed Dean of the School of Information Studies (iSchool) in 2008, and held the position for over ten years. She temporarily left the role in 2015. The school was transformed under her leadership, increasing the enrollment of students by over 70% and launching a graduate certificate in data science. She raised over $20 million to support research and development at Syracuse University. She chaired the iSchool Organization, which connects information science schools all over the world, from 2012 to 2014. Liddy worked to increase the representation of women at the iSchool, through initiatives such as the IT Girls Overnight Retreat – an annual weekend to introduce high school girls to Information Technology. She improved the career development programs of students at Syracuse University, increasing student employment to almost 100% post graduation. Liddy retired as Dean of the iSchool in 2019. === Selected innovations === US 6026388, Liddy, Elizabeth D., "User interface and other enhancements for natural language information retrieval system and method", published August 16, 1995, issued February 15, 2000 US 5963940, Liddy, Elizabeth D., "Natural language information retrieval system and method", published August 16, 1995, issued October 5, 1999 US 6006221, Liddy, Elizabeth D., "Multilingual document retrieval system and method using semantic vector matching", published August 16, 1995, issued December 21, 1999 == Personal life and death == Liddy was married shortly after graduating Daemen College in 1966. She had three children. Liddy died in Charlotte, North Carolina, on August 21, 2025, at the age of 81.

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  • AI Photo Editors Reviews: What Actually Works in 2026

    AI Photo Editors Reviews: What Actually Works in 2026

    Curious about the best AI photo editor? An AI photo editor is software that uses machine learning to help you get more done — it combines speed, accuracy, and an interface that just works. Hands-on testing shows real-world results vary, so a short free trial is the smartest way to decide. Whether you are a beginner or a pro, the right AI photo editor slots into your workflow and pays for itself fast. This guide breaks down the top picks, their pros and cons, and who each one is best for.

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  • Adobe PhotoDeluxe

    Adobe PhotoDeluxe

    PhotoDeluxe was a consumer-oriented image editing software line published by Adobe Systems from 1996 until July 8, 2002. At that time it was replaced by Adobe's newly launched consumer-oriented image editing software Photoshop Elements. Adobe no longer provides technical support for the PhotoDeluxe software line. PhotoDeluxe had a range of image processing capabilities for the home photographer and image handler. These included removing red-eye, cropping, and adjusting brightness, contrast, and sharpness. It also included software to extract pictures from an image scanner. Among the functionality included was the ability to dynamically resize photos and export them in a wide range of formats. It also had a range of printing options including printing multiple copies of an image on the same page. It was often bundled free with Epson scanners or as free software with new computers. == Features == Despite the critical concerns regarding the quality of the setup, Photo Deluxe supports layering, blurs, sharpening, cloning, gradient fills, color and background switches, color variations, resizing options, and many other features. Another drawback of PhotoDeluxe was that it was designed for Mac computers, so working on Windows PC was a problem for those who were unable to customize their preferences. == Versions == === Adobe PhotoDeluxe 1.0 === The first version was released in 1996 for Windows and Macintosh computers. In one year, it sold over one million copies. === Adobe PhotoDeluxe 2.0 === The new version was released in 1997 and had added features such as a Clone Tool, red-eye removal, and sample templates for making posters, cards, and calendars. It also had new special effect features. === Adobe PhotoDeluxe 3.0 === The 3rd version was released in 1998. The new features included customizable clipart settings, the ability to import photos on the web, enhanced repair activities following Guided Activities, and Adobe Connectables to add new activities. === Adobe PhotoDeluxe Home Edition (4.0) === Version 4.0 was created by the makers of Photoshop. It had advanced abilities such as tools to add animation, voice, and music to a picture. It also had features to restore photos to their original position. == History == Adobe PhotoDeluxe 1.0 was released in 1996 for Macintosh computers, initially retailing for an MSRP of $49. The software did quite well, reportedly selling over a million copies by February of the next year, primarily due to bundles with companies like Apple and Hewlett-Packard. PhotoDeluxe was primarily advertised to consumers as a way to do basic photo manipulation, such as cropping and rotating images, or creating simple cards and calendars. PhotoDeluxe 2.0 was released in 1997, and was the last version of PhotoDeluxe that Adobe made that worked on Macs. PhotoDeluxe 2.0 became the "number one selling consumer photo-editing software product in the world." PhotoDeluxe 3.0 was released in 1998, where it was rebranded as "3.0 Home Edition", as Adobe released PhotoDeluxe Business Edition later that year for a higher price. PhotoDeluxe Home Edition, unofficially called PhotoDeluxe 4.0, was released in 1999 and was the last version of PhotoDeluxe to be released. Adobe officially cancelled PhotoDeluxe on July 8, 2002, citing the presence of Photoshop and Photoshop Elements, with support being officially cancelled in mid-2003. No version of PhotoDeluxe is compatible with Windows 10, rendering the program obsolete. == Pricing == All home versions of PhotoDeluxe retailed for an MSRP of $49. PhotoDeluxe 2.0 and onwards allowed users to upgrade from a previous version of PhotoDeluxe or a competing piece of graphics software for $39. Additionally PhotoDeluxe Business Edition allowed a similar deal, allowing users to upgrade from other versions of PhotoDeluxe or a competing software for $59, instead of its normal price of $99. Adobe also offered a bundle allowing users of 1.0 or 2.0 to get 3.0 and Business Edition for $79.

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  • Eurotra

    Eurotra

    Eurotra was a machine translation project established and funded by the European Commission from 1978 until 1992. == History == In 1976, the European Commission started using the commercially developed machine translation system SYSTRAN with a plan to make it work for further languages than originally developed for (Russian-English and English-French), which however turned out to be difficult. This and the potential in existing systems within European research center, led to the decision in 1978 to start the project Eurotra, first through a preparatory Eurotra Coordination Group. Four years later, the European Commission and coordination group gained the approval of the European Parliament. The goal of the project as to create machine translation system for the official languages of the European Community, which at the time were Danish, Dutch, German, English, French, Italian, later including Greek, Spanish and Portuguese. However, as time passed, expectations became tempered; "Fully Automatic High Quality Translation" was not a reasonably attainable goal. The true character of Eurotra was eventually acknowledged to be in fact pre-competitive research rather than prototype development. The project was motivated by one of the founding principles of the EU: that all citizens had the right to read any and all proceedings of the Commission in their own language. As more countries joined, this produced a combinatorial explosion in the number of language pairs involved, and the need to translate every paper, speech and even set of meeting minutes produced by the EU into the other eight languages meant that translation rapidly became the overwhelming component in the administrative budget. To solve this problem Eurotra was devised. The project was unusual in that rather than consisting of a single research team, it had member groups distributed around the member countries, organised along language rather than national lines (for example, groups in Leuven and Utrecht worked closely together), and the secretariat was based at the European Commission in Luxembourg. The actual design of the project was unusual as MT projects go. Older systems, such as SYSTRAN, were heavily dictionary-based, with minor support for rearranging word order. More recent systems have often worked on a probabilistic approach, based on parallel corpora. Eurotra addressed the constituent structure of the text to be translated, going through first a syntactic parse followed by a second parse to produce a dependency structure followed by a final parse with a third grammar to produce what was referred to internally as Intermediate Representation (IR). Since all three modules were implemented as Prolog programs, it would then in principle be possible to put this structure backwards through the corresponding modules for another language to produce a translated text in any of the other languages. However, in practice this was not in fact how language pairs were implemented. The first "live" translation occupied a 4Mb Microvax running Ultrix and C-Prolog for a complete weekend some time in early 1987. The sentence, translated from English into Danish, was "Japan makes computers". The main problem faced by the system was the generation of so-called "Parse Forests" - often a large number of different grammar rules could be applied to any particular phrase, producing hundreds, even thousands of (often identical) parse trees. This used up huge quantities of computer store, slowing the whole process down unnecessarily. While Eurotra never delivered a "working" MT system, the project made a far-reaching long-term impact on the nascent language industries in European member states, in particular among the southern countries of Greece, Italy, Spain, and Portugal. There is at least one commercial MT system (developed by an academic/commercial consortium in Denmark) derived from Eurotra technology.

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  • Mark Keane (cognitive scientist)

    Mark Keane (cognitive scientist)

    Mark Thomas Gerard Keane (Irish: Marcus Ó Cathain, born 3 July 1961, Dublin, Ireland) is a cognitive scientist and author of several books on human cognition and artificial intelligence, including Cognitive Psychology: A Student's Handbook (8 editions, with Michael Eysenck), Advances in the Psychology of Thinking (1992, with Ken Gilhooly), Novice Programming Environments (1992/2018, with Marc Eisenstadt and Tim Rajan), Advances in Case-Based Reasoning (1995, with J-P Haton and Michel Manago)., Case-Based Reasoning: Research & Development (2022, with N Wiratunga). == Education == Keane received a B.A. in Psychology from University College Dublin in 1982. He then received a Ph.D. from Trinity College Dublin in 1987. He then moved to postdoctoral positions in Queen Mary University of London and the Open University. == Academic career == He was a Lecturer in Psychology at Cardiff University. He became a lecturer in Computer Science at Trinity College Dublin in 1990, and became a fellow in 1994. Keane moved to become Chair of Computer Science at University College Dublin in 1998. In 2006, he was seconded to Science Foundation Ireland as Director of ICT, overseeing on a $700m research investment. He advised the Irish Government on its 3.7B euro Strategy for Science, Technology & Innovation (SSTI). From 2006 to 2007, he was Director General of Science Foundation Ireland before returning to University College Dublin where he was appointed VP of Innovation & Partnerships (2007-2009). Keane's research has been split between cognitive science and computer science. His cognitive science research has been in analogy, metaphor, conceptual combination and similarity. His computer science research has been in natural language processing, machine learning, case-based reasoning, text analytics and explainable artificial intelligence. He has been a PI in the Science Foundation Ireland funded Insight Centre for Data Analytics working on digital journalism and digital humanities. More recently, he was deputy director of the VistaMilk SFI Research Centre that is exploring precision agriculture in the dairy sector.

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  • AI Video Editors Reviews: What Actually Works in 2026

    AI Video Editors Reviews: What Actually Works in 2026

    Curious about the best AI video editor? An AI video editor is software that uses machine learning to help you get more done — it combines speed, accuracy, and an interface that just works. Hands-on testing shows real-world results vary, so a short free trial is the smartest way to decide. Whether you are a beginner or a pro, the right AI video editor 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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  • Multicloud

    Multicloud

    Multicloud (also written as multi-cloud or multi cloud) is a term with varying interpretations, generally referring to a system using multiple cloud computing providers. According to ISO/IEC 22123-1: "multi-cloud is a cloud deployment model in which a customer uses public cloud services provided by two or more cloud service providers". Multi-cloud can involve various deployment models, including public, private, and hybrid clouds, and multiple service models, such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). Multicloud incorporates workload, data, traffic, and workflow portability options, which can result in varying implementation complexity. When effectively implemented, multicloud solutions can enhance architectural resilience, reduce dependence on a single vendor, and improve flexibility by leveraging services from different providers. However, multicloud strategies also present challenges, including increased operational complexity, security risks, higher costs, and integration difficulties. According to the 2024 State of the Cloud Report by Flexera, multi-cloud adoption has continued to rise in 2024. Enterprises increasingly silo applications into specific clouds and select best-fit services. Key use cases include data analysis in separate clouds and cross-cloud disaster recovery. == Advantages and challenges == There are several advantages to using a multicloud approach, including the ability to negotiate better pricing with cloud providers, the ability to quickly switch to another provider if needed, and the ability to avoid vendor lock-in. Multicloud can also be a good way to hedge against the risks of obsolescence, as it allows you to rely on multiple vendors and open standards, which can prolong the life of your systems. Additional benefits of the multicloud architecture include adherence to local policies that require certain data to be physically present within the area/country, geographical distribution of processing requests from physically closer cloud unit which in turn reduces latency and protect against disasters. Various issues and challenges also present themselves in a multicloud environment. Security and governance is more complicated, and more "moving parts" may create resiliency issues. == Difference between multicloud and hybrid cloud == Multicloud differs from hybrid cloud in that it refers to multiple cloud services from different vendors rather than multiple deployment modes (on-premises hardware, and public and private, cloud hosting). However, when considering a broad definition of multi-cloud, hybrid cloud can still be regarded as a special form of multi-cloud.

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  • Margaret Mitchell (scientist)

    Margaret Mitchell (scientist)

    Margaret Mitchell is a computer scientist who works on algorithmic bias and fairness in machine learning. She is most well known for her work on automatically removing undesired biases concerning demographic groups from machine learning models, as well as more transparent reporting of their intended use. == Education == Mitchell obtained a bachelor's degree in linguistics from Reed College, Portland, Oregon, in 2005. After having worked as a research assistant at the OGI School of Science and Engineering for two years, she subsequently obtained a Master's in Computational Linguistics from the University of Washington in 2009. She enrolled in a PhD program at the University of Aberdeen, where she wrote a doctoral thesis on the topic of Generating Reference to Visible Objects, graduating in 2013. == Career and research == Mitchell is best known for her work on fairness in machine learning and methods for mitigating algorithmic bias. This includes her work on introducing the concept of 'Model Cards' for more transparent model reporting, and methods for debiasing machine learning models using adversarial learning. Margaret Mitchell created the framework for recognizing and avoiding biases by testing with a variable for the group of interest, predictor and an adversary. In 2012, Mitchell joined the Human Language Technology Center of Excellence at Johns Hopkins University as a postdoctoral researcher, before taking up a position at Microsoft Research in 2013. At Microsoft, Mitchell was the research lead of the Seeing AI project, an app that offers support for the visually impaired by narrating texts and images. In November 2016, she became a senior research scientist at Google Research and Machine intelligence. While at Google, she founded and co-led the Ethical Artificial Intelligence team together with Timnit Gebru. In May 2018, she represented Google in the Partnership on AI. In February 2018, she gave a TED talk on "How we can build AI to help humans, not hurt us". In January 2021, after Timnit Gebru's termination from Google, Mitchell reportedly used a script to search through her corporate account and download emails that allegedly documented discriminatory incidents involving Gebru. An automated system locked Mitchell's account in response. In response to media attention Google claimed that she "exfiltrated thousands of files and shared them with multiple external accounts". After a five-week investigation, Mitchell was fired. Prior to her dismissal, Mitchell had been a vocal advocate for diversity at Google, and had voiced concerns about research censorship at the company. In late 2021, she joined AI start-up Hugging Face. Mitchell is a co-founder of Widening NLP, a special interest group within the Association for Computational Linguistics (ACL) seeking to increase the proportion of women and minorities working in natural language processing; and Computational Linguistics and Clinical Psychology, an annual workshop within the ACL that brings together clinicians and computational linguists to advance the state of the art in clinical psychology.

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  • Global Language Monitor

    Global Language Monitor

    The Global Language Monitor (GLM) is a company based in Austin, Texas, that analyzes trends in the English language. == History == Founded in Silicon Valley in 2003 by Paul J.J. Payack, the GLM describes its role as "a media analytics company that documents, analyzes and tracks cultural trends in language the world over, with a particular emphasis upon International and Global English". In April 2008, GLM moved its headquarters from San Diego to Austin. In July 2020, GLM announced that the word covid was its Top Word of 2020 for English. The company has been repeatedly criticized by linguists for promoting misinformation about language. Writing on Language Log, the linguist Ben Zimmer accused it of "hoodwink[ing] unsuspecting journalists on a range of pseudoscientific claims".

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  • AI Clip Makers: Free vs Paid (2026)

    AI Clip Makers: Free vs Paid (2026)

    Shopping for the best AI clip maker? An AI clip maker is software that uses machine learning to help you get more done — it keeps getting smarter as the underlying models improve. Pricing, accuracy, and the size of the model behind the tool are the three factors that most affect daily usefulness. Whether you are a beginner or a pro, the right AI clip maker 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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  • Lucy–Hook coaddition method

    Lucy–Hook coaddition method

    The Lucy–Hook coaddition method is an image processing technique for combining sub-stepped astronomical image data onto a finer grid. The method allows the option of resolution and contrast enhancement or the choice of a conservative, re-convolved, output. Tests with very deep Hubble Space Telescope Wide Field and Planetary Camera 2 (WFPC2) imaging data of excellent quality show that these methods can be very effective and allow fine-scale features to be studied better than on the unprocessed images. The Lucy–Hook coaddition method is an extension of the standard Richardson–Lucy deconvolution iterative restoration method. For many purposes it may be more convenient to combine dithered datasets using the Drizzle method.

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  • AI Writing Assistants: Free vs Paid (2026)

    AI Writing Assistants: Free vs Paid (2026)

    Curious about the best AI writing assistant? An AI writing assistant is software that uses machine learning to help you get more done — it combines speed, accuracy, and an interface that just works. Hands-on testing shows real-world results vary, so a short free trial is the smartest way to decide. Whether you are a beginner or a pro, the right AI writing assistant slots into your workflow and pays for itself fast. This guide breaks down the top picks, their pros and cons, and who each one is best for.

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  • Ocrad

    Ocrad

    Ocrad is an optical character recognition program and part of the GNU Project. It is free software licensed under the GNU GPL. Based on a feature extraction method, it reads images in portable pixmap formats known as Portable anymap and produces text in byte (8-bit) or UTF-8 formats. Also included is a layout analyser, able to separate the columns or blocks of text normally found on printed pages. == User interface == Ocrad can be used as a stand-alone command-line application or as a back-end to other programs. Kooka, which was the KDE environment's default scanning application until KDE 4, can use Ocrad as its OCR engine. Since conversion to newer Qt versions, current versions of KDE no longer contain Kooka; development continues in the KDE git repository. Ocrad can be also used as an OCR engine in OCRFeeder. == History == Ocrad has been developed by Antonio Diaz Diaz since 2003. Version 0.7 was released in February 2004, 0.14 in February 2006 and 0.18 in May 2009. It is written in C++. Archives of the bug-ocrad mailing list go back to October 2003.

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