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

SBIR 第二阶段：使用帧内和帧间机器学习对文档进行实时、准确的 OCR

• 批准号: 0924549
• 负责人: Ari Gross
• 金额: $ 50万
• 依托单位: CVISION Technologies, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0924549&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Real; time

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.

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）第二阶段项目涉及从文档中开发光学字符识别（OCR）的实时算法。该实时识别（RT/OCR）系统将根据SBIR合同进行全面开发，其识别速度比当前的商业系统快一个数量级，并将允许嵌入系统设备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，对于包括颜色、图形和多种字体的复杂文档，它将更加准确。当这项技术在SBIR奖的第二阶段成功商业化时，它可以部署在每一个企业的MFP和数码复印机设备上，将企业纸张转换为可搜索的电子文件，使我们更接近无纸化办公。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帧内和帧间机器学习的方法。要开发的算法不以任何方式特定于语言，并且几乎可以在任何平台上运行（例如服务器或手持设备）。其基本技术与目前商业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技术，将OCR技术提升到一个新的水平。这项技术旨在弥合纸张和数字媒体之间的差距，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，捕捞业将增长到24.2亿美元，复合年增长率为16.4%。用于自动化和半自动化字段编码的实时OCR解决了仅在美国就使用145亿美元/年手工劳动力的行业的需求。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。这种识别技术在第二阶段研究和开发的背景下成功开发和商业化后，可以推广和扩展到处理实时视频识别，并应用于自主车辆导航，视障人士辅助设备和机器人工厂自动化。