SBIR Phase I: Real-time, accurate OCR from Video using Intra- and Inter-Frame Machine Learning

SBIR 第一阶段：使用帧内和帧间机器学习对视频进行实时、准确的 OCR

• 批准号: 0810693
• 负责人: Ari Gross
• 金额: $ 10万
• 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: 2008
• 资助国家: 美国
• 起止时间: 2008-07-01 至 2008-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0810693&HistoricalAwards=false
• 关键词: SBIR; Phase; Real; time; accurate

This Small Business Innovation Research (SBIR) Phase I research project focusses on the development of ground-breaking real-time algorithms for automatically finding and recognizing text in digital video of complex 3-D environments using machine learning of fonts and text strings. Essentially, the project takes OCR from being a technology for 2-D documents and brings it to the 3-D world. The project builds on algorithms for optical character recognition (OCR) of documents where conventional OCR fails: colorful brochures, magazine covers, and other sources where photographs, line art, and arbitrarily-rotated text greatly complicate the OCR process. The project aims to build on this technology to find solutions to the finding and recognizing text in complex 3-D real world scenes such as street signs and storefronts where the text may be at any arbitrary 3-D angle to the camera. Critical to the success of this project is the algorithm's capability for machine learning of fonts. There are a number of exciting applications that are impacted by accurate OCR from video sources. While OCR of text in video sources can be done, it usually must be on plainly obvious text, such as subtitles, and it cannot be done in real-time. Real-time and accurate video OCR would enable applications that include 1) Unaided indexing of digital video footage by the text contained therein, 2) aiding the blind navigate independently, both indoors and outdoors, 3) automated continuous roadside or vehicle based license plate scanning, and 4) as ground truth for improved GPS accuracy. Markets for the technology therefore include individuals, corporations, and government agencies. The societal impacts include 1) rendering digitized video libraries searchable by more metadata tags at low cost, 2) greater independence and safety for the blind, 3) improving road safety through automatically identifying cars reported stolen or cars owned by people with suspended licenses, and 4) improved GPS navigation accuracy. Technological impacts will be in the areas of machine learning applied to video OCR, real-time OCR, and low-resolution OCR.

这个小型企业创新研究（SBIR）第一阶段研究项目的重点是开发突破性的实时算法，用于使用字体和文本字符串的机器学习来自动查找和识别复杂3D环境中的数字视频中的文本。从本质上讲，该项目将OCR从2D文档的技术带到了3D世界。该项目建立在光学字符识别（OCR）的算法之上，传统OCR无法识别的文档包括彩色小册子、杂志封面以及其他来源，其中照片、线条艺术和任意旋转的文本使OCR过程变得非常复杂。该项目旨在建立在这项技术的基础上，找到在复杂的3-D真实的世界场景中寻找和识别文本的解决方案，例如街道标志和店面，其中文本可能与摄像机成任意3-D角度。该项目成功的关键是该算法的字体机器学习能力。有许多令人兴奋的应用程序受到来自视频源的准确OCR的影响。虽然可以对视频源中的文本进行OCR，但它通常必须针对明显的文本，例如字幕，并且不能实时完成。实时和准确的视频OCR将使应用包括：1）通过数字视频片段中包含的文本对数字视频片段进行无辅助索引，2）帮助盲人在室内和室外独立导航，3）自动连续的路边或基于车辆的车牌扫描，以及4）作为地面实况以提高GPS精度。因此，该技术的市场包括个人、公司和政府机构。社会影响包括：1）使数字化视频库可通过更多元数据标签以低成本进行搜索，2）盲人的独立性和安全性更高，3）通过自动识别报告被盗的汽车或被吊销执照的人拥有的汽车来改善道路安全，以及4）提高GPS导航精度。技术影响将出现在应用于视频OCR、实时OCR和低分辨率OCR的机器学习领域。