Our second example deals with a more challenging problem: the recognition of hand-printed letters of the alphabet. The characters that people print in the ordinary course of filling out forms and questionnaires are surprisingly varied. Gaps abound wherecontinuous lines might be expected; curves and sharp angles appear interchangeably; there is almost every imaginable distortion of slant, shape and size. Even human readers cannot always identify such characters; their error rate is about 3 per cent on randomly selected letters and numbers, seen out of context.
– from Oliver G. Selfridge & Ulric Neisser. PATTERN RECOGNITION BY MACHINE . In Computers & thought, Edward A. Feigenbaum and Julian Feldman (Eds.). MIT Press, Cambridge, MA, USA, 1963. pp. 8-30.
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.
TL;DR: A lifetime subscription to TexTalky AI Text-to-Speech is on sale for £28.08, saving you 93% on list price. From marketing content and video narration to customer support and tutorials, there are many instances in today's marketplace when a professional human voice is needed. But due to time constraints, lack of proper recording equipment, or simply the fact you hate your voice, you may turn to a text-to-speech software. Sometimes the robotic voices from these apps leave a lot to be desired. TexTalky AI Text-to-Speech aims to convert your text to lifelike human voices in just a few seconds.
Envision announced that its AI-powered smart glasses will soon be upgraded with improved Optical Character Recognition (OCR), better text recognition with contextual intelligence, support for additional languages, and the creation of a third-party app ecosystem. According to Envision, the new ecosystem will allow for the "easy integration of specialist services, such as indoor and outdoor navigation, to the Envision platform." Envision based its smart glasses on the Enterprise Edition of Google Glass, using its built-in camera and processing power to help support its mission of accepting and processing visual data to help the visually impaired recognize objects and their surroundings. While Google Glass failed to gain widespread consumer traction across its multiple releases, it has since found a home within niche use cases such as Envision's repurposing it as a hardware vehicle for its AI-based platform. Other attempts have been made in the past at using AR (Augment Reality) technology to help those with visual impairments.
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.