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Optical Character Recognition

States, activists sue USPS over purchase of gas-powered mail trucks


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) in Python


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.

Optical Character Recognition


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.

A lifetime subscription to this intuitive text-to-speech software is on sale for under £30


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.

Updated Envision smart glasses add improved OCR, new languages, third-party app support


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.

OCR Plus AI Opens New Vistas


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. 

Minute Article - Member Blogs - By Madhavi Desai


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.



Recent Deep Learning advancements, such as the introduction of transformer topologies, have helped us accelerate our handwritten character recognition. Intelligent Character Recognition (ICR), is a term used to describe the process for recognizing handwritten content. ICR algorithms require more intelligence than ordinary OCR. This post will cover the challenges of handwritten text identification and the techniques that can be used to tackle them using deep learning and machine learning. In the healthcare/pharmaceutical industry, patient medication digitization is a serious issue. Roche processes millions of PDFs each day, processing petabytes in medical PDFs.

Hindi Character Recognition


Character recognition is a process that allows computers to recognize written or printed characters such as numbers or letters and to change them into a form that computers can use. As a part of this case study, we are going to recognize "Hindi characters". It is a Character Recognition problem related to computer vision, where our task is to predict the Hindi character present in the image. The Model should predict or recognize the character present in the image in real-time. So the latency of the model should be low.