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

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。这个小企业创新研究（SBIR）第二阶段项目涉及开发从文件中进行光学字符识别（OCR）的实时算法。这种实时识别（RT/OCR）系统将根据SBIR合同进行全面开发，其识别速度比目前的商用系统快一个数量级，并且可以嵌入到系统设备中并在捕获时完成实时识别。RT/OCR系统在识别精度上也不会有任何损失，事实上，对于包含颜色、图形和多种字体的复杂文档，识别精度会更高。当这项技术在SBIR合同的第二阶段成功商业化时，它可以部署在每个公司的MFP和数字复印机设备上，将公司纸张转换为可搜索的电子文件，使我们离无纸化办公更近一步。我们打算在开发这个实时OCR识别系统中使用的技术是基于使用帧内和帧间机器学习的方法。要开发的算法在任何方面都不是特定于语言的，并且可以在几乎任何平台上运行（例如服务器或手持设备）。其基本技术与目前商用OCR识别系统的识别内核完全不同。该项目专注于开发革命性的技术，将OCR技术提升到一个新的水平。该技术旨在弥合纸质媒体和数字媒体之间的鸿沟，是账单支付机（BMP）、文档捕获和文档处理行业急需的引擎。到2010年，捕集产业将增长到24.2亿美元，复合年增长率为16.4%。用于自动化和半自动现场编码的实时OCR解决了仅在美国每年使用145亿美元手工劳动力的行业需求。RT/OCR将成为解决复杂文档手动纸质索引的解决方案的一部分，每年可能为行业和政府节省数十亿美元。该识别技术在二期研发成功并实现商业化后，可以推广和扩展到实时视频识别，应用于自动驾驶汽车导航、视障人士辅助、机器人工厂自动化等领域。

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.