Feitian Technologies showed off its newest portfolio of four Android handheld devices, three of which include fingerprint biometrics, and which support an assortment of applications from law enforcement to voting. The Handheld Biometric Identification Terminal (V11) is a wireless, five-inch terminal with fingerprint, iris, and face biometric verification. Customers can choose between fingerprint sensors certified for single flat fingers at FAP30, FAP20, or FAP10, from Integrated Biometrics, Suprema, Idemia, Futronic, Aratek and SecuGen, according to the product page. The device also supports scanning of digital identity documents through NFC, MRZ Passport reading, and optical character recognition (OCR). The Multifunction Handheld Terminal (V12) terminal is intended for law enforcement that sports a fingerprint sensor with live finger detection, breathalyzer, and narcotics detector, and can issue tickets.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Before Dr. Alan Turing designed the first computer, people merely dreamed of intelligent machines that could read paperwork and do most of their grunge work for them. Science-fiction movies depict advanced software processing large amounts of documents to find hidden insights that save the day. Today this is available in real life from progressive-thinking software providers. One of them, San Francisco-based Automation Hero, today launched v6.0 of its Hero Platform, a SaaS service the company claims takes a quantum leap in OCR (optical character recognition) document-processing accuracy.

A significant enhancement has been made to PaddleOCR, the multilingual optical character recognition (OCR) toolkits. With over 80 different multi-language recognition models and an easy-to-use interface, PaddleOCR is an open-source OCR repository worth checking out. OCRv3 PP-OCRv3 has a 5% to 11% increase in accuracy in English and multilingual scenarios. Annotation functions for tables, irregular text pictures, and essential information extraction tasks have been added to PPOCRLabelv2. "Dive into OCR," a new interactive e-book, is now available.

The US Postal Service is facing more than just stern warnings over its decision to buy mostly gas-powered mail delivery trucks. Environmental activist groups (including the Center for Biological Diversity and the Sierra Club) and 16 states have filed lawsuits in California and New York State to challenge the Postal Service's Next Generation Delivery Vehicle purchasing decision. They argue the USPS's environmental review was flawed and illegal, ignoring the "decades of pollution" the combustion-engine trucks would produce. The USPS allegedly violated the National Environmental Policy Act by committing to buy 165,000 delivery vehicles (just 10 percent of them electric) without first conducting a "lawful" environmental review. The service only started its review six months after it had signed a contract, according to the California lawsuit.

Optical Character Recognition (OCR) with less than 10 Lines of Code using Python · Want to read more stories like this? It costs only 4,16$ per month. Within the area of Computer Vision is the sub-area of Optical Character Recognition (OCR), which aims to transform images into texts. OCR can be described as converting images containing typed, handwritten or printed text into characters that a machine can understand. It is possible to convert scanned or photographed documents into texts that can be edited in any tool, such as the Microsoft Word.

OCR (Optical Character Recognition) is a technology that enables the conversion of document types such as scanned paper documents, PDF files or pictures taken with a digital camera into editable and searchable data. OCR creates words from letters and sentences from words by selecting and separating letters from images. If you don't have any prior knowledge, I can recommend it. This is a slightly polished and packaged version of the Keras CRNN implementation and the published CRAFT text detection model. It provides a high level API for training a text detection and OCR pipeline.

In today's information age, impacts of digital transformations are present across varying industries and sectors.

Effects of Optical Character Recognition (OCR) quality on historical information retrieval have so far been studied in data-oriented scenarios regarding the effectiveness of retrieval results. Such studies have either focused on the effects of artificially degraded OCR quality (see, e.g., [1-2]) or utilized test collections containing texts based on authentic low quality OCR data (see, e.g., [3]). In this paper the effects of OCR quality are studied in a user-oriented information retrieval setting. Thirty-two users evaluated subjectively query results of six topics each (out of 30 topics) based on pre-formulated queries using a simulated work task setting. To the best of our knowledge our simulated work task experiment is the first one showing empirically that users' subjective relevance assessments of retrieved documents are affected by a change in the quality of optically read text. Users of historical newspaper collections have so far commented effects of OCR'ed data quality mainly in impressionistic ways, and controlled user environments for studying effects of OCR quality on users' relevance assessments of the retrieval results have so far been missing. To remedy this The National Library of Finland (NLF) set up an experimental query environment for the contents of one Finnish historical newspaper, Uusi Suometar 1869-1918, to be able to compare users' evaluation of search results of two different OCR qualities for digitized newspaper articles. The query interface was able to present the same underlying document for the user based on two alternatives: either based on the lower OCR quality, or based on the higher OCR quality, and the choice was randomized. The users did not know about quality differences in the article texts they evaluated. The main result of the study is that improved optical character recognition quality affects perceived usefulness of historical newspaper articles significantly. The mean average evaluation score for the improved OCR results was 7.94% higher than the mean average evaluation score of the old OCR results.

AI-powered optical character recognition lets insurers unlock vast troves of data and streamline all processes.||Insurers still struggle with PDFs, images and handwritten documents. Countless human hours are required to manually extract the data into a machine-readable format. This process is known as ETL (extract, transform and load). Insurers that can maximize their ETL capabilities have a powerful competitive advantage. 

Referred also as text recognition, the technology of OCR uses a scanner to convert the physical documents or images containing printed, typed or handwritten text into digitized text data that can be machine-readable. The OCR software converts the scanned images into a black and white version wherein black color represents the characters and white the background. With the help of pattern recognition to recognize the characters or feature recognition to detect the lines and strokes of the characters, characters are identified and converted into ASCII codes that can be easily handled by computer systems. OCR technology has become a business necessity helping businesses to transition towards digitalization by capturing, evaluating, and maintaining sensitive data and holding its promise of monitoring efficient workflow across various sectors.