OCR-IT Demo Android App and Source Code for Android OS by the host of OCR Cloud 2.0 API lets developers add the capability to process mobile images and create usable text documents to their Android apps. This sample app demonstrates how easily optical character recognition (OCR) can be implemented for Android OS applications and spur developers to make the leap to adding OCR to newly created applications for that platform.
Download the App, full source code for this app, and view screenshots here: Android App and Source Code
“The new OCR-IT Demo Android App, for the lack of better name, and its source code are designed to help app developers bring the power of OCR to their Android apps easily and seamlessly,” said company officials at OCR-IT. “In the onslaught of new apps for Android smart phones, we believe that those that integrate OCR will stand out for users. These capabilities add significant values since it lets users do more in less time by transforming paper-based documents into usable formats. We want to help more developers bring these capabilities to end users.”
The OCR-IT Demo Android App allows users to capture images of documents taken with a smart phone camera and to create a document library to house document images. The solution provides single-button access that extracts text in several predefined hard-coded languages in seconds. Results can be viewed in two hard-coded formats: Searchable PDF and Plain Text TXT. In addition, the OCR-IT Demo Android App offers links to external pages that provide additional information about OCR in the Cloud, as well as transparent details that allow developers to see OCR processing in action.
Ilya Evdokimov is a long-term practitioner and expert in leading Optical Character Recognition (OCR), Data Capture and Document Processing techniques, technologies and solutions. With over 15 years of experience spanning enterprise software implementations, mobile applications development, cloud-based systems integration and desktop-level automation, Ilya Evdokimov uses through industry knowledge and experience to achieve high efficiency and workflow optimization in most challenging paper-dependent and digital image capture environments.