Today, Amazon Web Services (AWS) announced that Amazon Textract, a machine learning service that quickly and easily extracts text and data from forms and tables in scanned documents, is now eligible for healthcare and life science workloads that require HIPAA compliance. This launch builds upon the existing portfolio of AWS artificial intelligence services that are HIPAA-eligible, including Amazon Translate, Amazon Comprehend, Amazon Transcribe, Amazon Polly, Amazon SageMaker and Amazon Rekognition – that help customers retrieve data from documents more accurately to reach better healthcare decisions, operate more efficiently, and help identify medical and scientific trends. Critical healthcare information often lies within documents such as medical records and forms. Healthcare and life science organizations need to access data that is locked inside those documents in order to fulfil medical claims, streamline administrative processes, and process electronic health records. They routinely extract text and data from documents through manual data entry or simple optical character recognition (OCR) software.

Today, Amazon Web Services (AWS) announced that Amazon Textract, a machine learning service that quickly and easily extracts text and data from scanned documents is now eligible for healthcare workloads that require HIPAA certification. This launch builds upon the existing portfolio of AWS artificial intelligence services that are HIPAA-eligible, including Amazon Translate, Amazon Comprehend, Amazon Transcribe, Amazon Polly, Amazon SageMaker and Amazon Rekognition – that help deliver better healthcare outcomes. Healthcare providers routinely extract text and data from documents such as medical records and forms through manual data entry or simple optical character recognition (OCR) software. This is a time-consuming and often inaccurate process that produces outputs requiring extensive post-processing before it can be used by other applications. What organizations want instead is the ability to accurately identify and extract text and data from forms and tables in documents of any format and from a variety of file types and templates.

An open-source disaster response tool that uses visual recognition and learns through artificial intelligence and cloud tools began as an idea that a self-taught developer had at IBM's Call for Code hackathon in Puerto Rico last year. IBM announced DroneAid on Oct. 2 as an open-source project through Code and Response, the company's $25 million program dedicated to the creation and deployment of open-source solutions tackling real-world problems. DroneAid uses visual recognition technology to detect and count SOS icons on the ground gleaned from drone video streams and automatically plots the emergency needs on a map for first responders. Developer Pedro Cruz had planned to use optical character recognition to detect messages, but reading different handwriting and languages complicated that approach. Instead, the tool relies on a subset of the U.N. Office for the Coordination of Humanitarian Affairs' 500 humanitarian icons – symbols that DroneAid can learn and first responders can quickly understand.

Have you ever thought to yourself "I just made a great MNIST classifier! While the handwritten digits dataset is a great, clean way to get into machine learning (on the classification side, anyway), it is rightly dubbed the "Hello World" of the field. You can use it to make a sensible ML pipeline and learn how to implement different kinds of models, but it doesn't have much use past that… until now. One of my first posts here used some basic python data structures and logic to solve Sudoku puzzles about twice as fast as you could blink, but I had to manually enter the numbers into the arrays to prepare the solver. In this post, I'd like to get into how to use some image processing tools and a convolutional neural net to function for optical character recognition (OCR).

Amharic is the official language of the Federal Democratic Republic of Ethiopia. There are lots of historic Amharic and Ethiopic handwritten documents addressing various relevant issues including governance, science, religious, social rules, cultures and art works which are very reach indigenous knowledge. The Amharic language has its own alphabet derived from Ge'ez which is currently the liturgical language in Ethiopia. Handwritten character recognition for non Latin scripts like Amharic is not addressed especially using the advantages of the state of the art techniques. This research work designs for the first time a model for Amharic handwritten character recognition using a convolutional neural network. The dataset was organized from collected sample handwritten documents and data augmentation was applied for machine learning. The model was further enhanced using multi-task learning from the relationships of the characters. Promising results are observed from the later model which can further be applied to word prediction.

The Mercury ViewPoint SmartVisor doesn't just magnify, with the touch of a button it reads out to you as well. ViewPoint SmartVisor is a breakthrough in technology for anyone suffering from restricted sight. It also works great for people with central vision loss e.g. ViewPoint SmartVisor sits comfortably on the head giving clear reproduced natural and enhanced images in the magnification of your choice. See everything clearly in full colour, enhanced full colour or with different coloured foregrounds and backgrounds.

Machine learning has played a very important role in the development of technology that has a large impact on our everyday lives. However, machine learning is also influencing the direction of technology that is not as commonplace. Text to speech technology is a prime example. Text to speech technology predates machine learning by over a century. However, machine learning has made the technology more reliable than ever. We live in an era where audiobooks are gaining more appreciation than the traditional pieces of literature.

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.