Optical Character Recognition

Have you ever faced challenges while creating user-oriented digital security algorithms? Designing a more efficient solution to replace the creation and maintenance of paperwork for numerous employees is certainly beneficial. However, even it the era of Data Science and Artificial Intelligence, reinventing security-related services is no easy task. Let's see the approach to develop software solutions with deep learning Optical Character Recognition (OCR) for processing US driver's licenses and IDs. This technology began with the scanning of books, text recognition and hand-written digits (NIST dataset).

Element AI, a company that builds artificial intelligence (AI) tools for enterprises, has raised CAD $200 million (USD $151 million) in a series B round of funding from a host of existing and new investors, including Gouvernement du Québec, Data Collective (DCVC), Hanwha Asset Management, BDC, Real Ventures, Caisse de dépôt et placement du Québec (CDPQ), and McKinsey & Company. Founded in 2016, Element AI develops AI software "that helps people work smarter," according to its marketing blurb. So far, the startup has focused on partnering with enterprises that want to use AI but lack the required expertise, connecting businesses with machine learning experts in-house and elsewhere to address specific problems. Earlier this year, Element AI officially launched its first products for enterprise customers in the form of "decision-making automation tools." Using computer vision, optical character recognition (OCR), and other AI mechanisms, Element AI promises to enable machines to do things like "read" documents or answer workers' questions about internal operations using natural language queries.

Whether it's auto-extracting information from a scanned receipt for an expense report or translating a foreign language using your phone's camera, optical character recognition (OCR) technology can seem mesmerizing. And while it seems miraculous that we have computers that can digitize analog text with a degree of accuracy, the reality is that the accuracy we have come to expect falls short of what's possible. And that's because, despite the perception of OCR as an extraordinary leap forward, it's actually pretty old-fashioned and limited, largely because it's run by an oligopoly that's holding back further innovation. OCR's precursor was invented over 100 years ago in Birmingham, England by the scientist Edmund Edward Fournier d'Albe. Wanting to help blind people "read" text, d'Albe built a device, the Optophone, that used photo sensors to detect black print and convert it into sounds.

A text-to-speech system, which converts written text into synthesized speech, is what allows Alexa to respond verbally to requests or commands. Through a service called Amazon Polly, text-to-speech is also a technology that Amazon Web Services offers to its customers. Last year, both Alexa and Polly evolved toward neural-network-based text-to-speech systems, which synthesize speech from scratch, rather than the earlier unit-selection method, which strung together tiny snippets of pre-recorded sounds. In user studies, people tend to find speech produced by neural text-to-speech (NTTS) systems more natural-sounding than speech produced by unit selection. But the real advantage of NTTS is its adaptability, something we demonstrated last year in our work on changing the speaking style ("newscaster" versus "neutral") of an NTTS system.

Huawei's Honor subsidiary today launched a new artificial intelligence (AI)-powered app called PocketVision, which is designed to help those with visual impairments read documents, menus, and text using their smartphone camera. The PocketVision app, which was unveiled today at the annual IFA conference in Berlin, was developed in conjunction with Eyecoming, a Chinese social technology company specializing in visual impairments. According to Census Bureau data, roughly 20% of people in the U.S alone have a disability, more than half of whom report a "severe" disability. This figure is roughly consistent with other countries around the world, too. And it's against that backdrop that Huawei's Honor offshoot is launching the PocketVision app.

Whether it's auto-extracting information from a scanned receipt for an expense report or translating a foreign language using your phone's camera, optical character recognition (OCR) technology can seem mesmerizing. And while it seems miraculous that we have computers that can digitize analog text with a degree of accuracy, the reality is that the accuracy we have come to expect falls short of what's possible. And that's because, despite the perception of OCR as an extraordinary leap forward, it's actually pretty old-fashioned and limited, largely because it's run by an oligopoly that's holding back further innovation. OCR's precursor was invented over 100 years ago in Birmingham, England by the scientist Edmund Edward Fournier d'Albe. Wanting to help blind people "read" text, d'Albe built a device, the Optophone, that used photo sensors to detect black print and convert it into sounds.

Huawei's Honor subsidiary today launched a new artificial intelligence (AI)-powered app called PocketVision, which is designed to help those with visual impairments read documents, menus, and text using their smartphone camera. The PocketVision app, which was unveiled today at the annual IFA conference in Berlin, was developed in conjunction with Eyecoming, a Chinese social technology company specializing in visual impairments. According to Census Bureau data, roughly 20% of people in the U.S alone have a disability, more than half of whom report a "severe" disability. This figure is roughly consistent with other countries around the world, too. And it's against that backdrop that Huawei's Honor offshoot is launching the PocketVision app.

This particular report intends to offer business leaders of the financial area with a concept of what they are able to presently expect from AI in the business of theirs. We wish this report allows business leaders in finance to garner insights they are able to confidently relay to the executive teams of theirs so they are able to make educated choices when thinking about AI adoption. At the minimum, we wish the article decreases the time industry leaders in finance invest researching AI businesses they might wish to work with. Leverton is a Berlin-based business with 112 employees. The company provides a software program which it says could help financial institutions and banks extract as well as access info from the contracts of theirs and leases using optical character recognition (Deep learning and ocr). Leverton claims computer users can install the software of theirs on enterprise computers and publish a zip file with the appropriate leases or maybe contracts that have to be abstracted into the product. Leverton's software program comes pre-made with regular formats for particular kinds of documents, like credit agreement, mortgage documents, and loan agreements.

Google LLC today updated its Cloud Text-to-Speech service with new languages and voices in order to make it useful to more of its customers. Google Cloud Text-to-Speech is intended to help companies develop better conversational interfaces for the services they supply. It works by transforming written text into artificial speech that's spoken in realistic human voices. With the service, Google is targeting three main markets: voice response systems for call centers; "internet of things" products such as car infotainment systems, TVs and robots; and applications such as podcasts and audiobooks, which convert text into speech. In a blog post, Google product manager Dan Aharon said Cloud Text-to-Speech is getting 12 new languages or variants, including Czech, English (India), Filipino, Finnish, Greek, Hindi, Hungarian, Indonesian, Mandarin Chinese (China), Modern Standard Arabic and Vietnamese.