A2iA Releases a2ia mNote™ for Handwritten Mobile Note Recognition

A2iA (@A2iA, @A2iAEurope), a world leading developer of handwritten and machine printed text recognition, information extraction and intelligent document classification toolkits, announced the availability of a2ia mNote™ , a new software toolkit that turns cursive handwritten notes into searchable, digital text by simply taking a photo of the paper document with a smartphone or tablet.

“For more than 25 years, A2iA’s research teams have been dedicated to data extraction and workflow automation, addressing a changing global market and new user requirements,” said Jean-Louis Fages, A2iA President & Chairman of the Board. “With the reliance on mobile devices in all facets of our lives, A2iA has released its newest product to address mobile content management and to further simplify mobile data recognition for the everyday user, by enabling handwriting recognition directly on the phone or tablet and directly from a mobile image.”

Powered by A2iA’s award-winning handwriting recognition capabilities, a2ia mNote brings unstructured and cursive text recognition to the mobile device as a new feature that can be integrated into mobile operating systems, apps, note taking solutions, and more. Without the need for a dictionary or customization, users can transcribe handwritten notes such as meeting notes, to-do or shopping lists, and handwritten letters.

Using the full text extraction from these documents, the integrated app can perform keyword search and retrieval of the handwritten data, even when the smartphone or tablet is offline and not connected to cellular service or Wi-Fi, as well as enable the data to be shared between devices or cloud-based solutions.

Ensuring a simple user experience, a2ia mNote also utilizes a2ia’s Auto-Locate™ feature to allow users the ability to capture a document quickly and accurately. Users simply hold the mobile device over a document and without a pre-defined zone or bounding box, and without a specific orientation in which to hold the document, a2ia mNote will automatically locate the edges of the document and capture it. Once captured, documents are automatically corrected, including their skew and orientation, coloring and lighting, and all noise is removed. Using this corrected document, all handwritten and printed information is extracted and transcribed into electronic, searchable and editable text for integration and use by a mobile app, enterprise or a cloud-based application.

About A2iA

Award-winning with research and development at its core, A2iA, Artificial Intelligence and Image Analysis (www.a2ia.com), is a science and R&D driven software company with deep roots in artificial intelligence, machine learning and neural networks. As the worldwide leading developer of handwritten and machine printed text recognition engines, we bring to market software toolkits (SDK) that are dedicated to image analysis and data extraction from everyday paper documents. Integrated into end-to-end partner applications or operating systems, A2iA’s technology is currently deployed in more than 42 countries around the world and allows complex and cursive data, including unstructured handwritten letters, to become part of a structured database, making it searchable and reportable, with the same level of flexibility as printed or digital data. Proven to reduce costs and nearly eliminate data-entry and manual document processing, A2iA’s technology has been improving business process automation and providing a visible ROI for more than 25 years as it enhances forms-processing, transaction-processing, content management, document management, and knowledge management systems worldwide. For more information, visit www.a2ia.com or call +1 917-237-0390 within the Americas, or +33 1 44 42 00 80 within EMEA or Asia.

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Contact information

A2iA
North America:
Wendi Klein, +1-917-237-0390
wendi.klein@a2ia.com
or
EMEA & India:
Venceslas Cartier, +33 1 44 42 00 92
v.cartier@a2ia.com
or
Latin America:
Jorge Tavares, +1-917-237-0390
jorge.tavares@a2ia.com