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.

Amazon Textract is a machine learning service that makes it easy to extract text and data from virtually any document. Textract goes beyond simple optical character recognition (OCR) to also identify the contents of fields in forms and information stored in tables. This allows you to use Amazon Textract to instantly "read" virtually any type of document and accurately extract text and data without the need for any manual effort or custom code. The blog post Automatically extract text and structured data from documents with Amazon Textract shows how to use Amazon Textract to automatically extract text and data from scanned documents without any machine learning (ML) experience. One of the use cases covered in the post is search and discovery.

Large and powerful Typhoon Hagibis is disrupting mail and parcel delivery services in Japan while forcing amusement facilities to suspend operations despite a three-day weekend through Monday, a public holiday. On Saturday, Japan Post Co. halted mail collection and delivery operations, and over-the-counter services at post offices mainly in the Kanto region including Tokyo due to suspensions of public transport services and other factors. With delays having already occurred in some mail and parcel delivery services, Japan Post is expecting the disruptions to spread throughout the country. Yamato Transport Co. halted or shortened the day's parcel pickup and delivery operations in many areas to ensure safety of workers. Delays in parcel deliveries are expected to continue until around Monday due in part to road clolsures, company officials said.

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.