SBIR Phase II: Real-time, Accurate OCR from Documents using Intra- and Inter-Frame Machine Learning

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This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).This Small Business Innovation Research (SBIR) Phase II project involves development of real-time algorithms for Optical Character Recognition (OCR) from documents. This real-time recognition (RT/OCR) system, to be fully developed under this SBIR award, performs recognition an order of magnitude faster than current commercial systems and will allow for real-time recognition that can be embedded on a system device and done at the time of capture. The RT/OCR system will also have no loss in recognition accuracy, and will, in fact, be more accurate for complex documents that include color, graphics, and multiple fonts. This technology, when successfully commercialized within Phase II of the SBIR award, could be deployed on every corporate MFP and digital copier device, converting corporate paper to searchable, electronic files and bringing us one step closer to the paperless office. The technology we intend to use in developing this real-time OCR recognition system is based on methods using Intra- and Inter-Frame Machine Learning. The algorithms to be developed are not, in any way, language specific and can run on virtually any platform (e.g. server or handheld device). The basic technology is completely different from the recognition kernels of current commercial OCR recognition systems.This project is focused on developing revolutionary technology that will take OCR technology to a new level. This technology is designed to bridge the gap between paper and digital media, a much needed engine for Bill Payment Machine (BMP), document capture and document processing industry. The capture industry will grow to $2.42 billion in 2010, a CAGR of 16.4%. Real-time OCR for automated and semi-automated field coding addresses the needs of an industry that uses $14.5 billion/year of manual labor just in the US. RT/OCR will be part of a solution that addresses manual paper-based indexing for complex documents, potentially saving the industry and the government billions of dollars every year. This recognition technology, after being successfully developed and commercialized within the context of the Phase II research and development, can be generalized and extended to handle real-time video recognition, with application to autonomous vehicle navigation, aids for the visually impaired, and robotic factory automation.