Error Reduction in Handwriting Recognition with applications

减少应用程序手写识别中的错误

• 批准号: RGPIN-2014-04330
• 负责人: Suen, Ching
• 金额: $ 2.84万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=634380
• 关键词: Error; Reduction; Handwriting; Recognition; applications

The objective of this program is to conduct advanced innovative research in computer recognition of handwritten characters, words, and symbols. Handwriting is one of the most challenging subjects in the field of pattern recognition due to the infinite varieties of character shapes and qualities. Although handwriting recognition systems have the potential of reading bank cheques, envelopes, archival documents, utility payment slips, income tax returns, and other business forms, very few are actually used at the moment, due to the high error rate of the current recognition systems. Indeed, banks and different companies are spending billions of dollars each week to enter handwritten data manually into the computer. In the past, most research was focused on high recognition rates with less emphasis on the most difficult and costly problems of error rates. This research picks up this challenge by investigating different methods of reducing the error rate to increase the reliability of recognitions, to produce a new generation of recognition systems for full deployment in practical environments.This research aims to increase the intelligence and capability of computers so that they can read, at high speed and with great accuracy, the information written on various types of documents where billions of dollars are being spent each week to manually enter the handwritten data from documents into the computer. Based on decades of experience plus a world renowned reputation in(a) conducting intense research in handwriting recognition,(b) having developed numerous high performance recognition systems, (c) having guided more than 190 graduate students, post-doctoral fellows, and visiting academic/industrial scientists, and(d) playing a lead role in this field and constantly interacting with prominent researchers around the world, the applicant proposes the following program to minimize the error rate and maximize the recognition score:1) To continue to investigate the causes of substitution errors and the drawbacks of current recognition systems,2) To explore and evaluate a variety of configurations of error-reduction schemes with a cascade of uncertainty-removal modules,3) To create very large databases and introduce new training techniques to separate good and bad samples,4) To discover distinctive features and vital parts of individual characters derived from eye-tracking and perceptual studies.5) To select dynamic sets of complementary features for various stages of multi-stage recognition systems to produce the most reliable and the best system to recognize handwritten data in different languages, English, French, Arabic, Chinese, etc.6) To integrate the error-reduced recognition systems with the high-performance systems, and discover the critical paths of optimizing their overall performance and trade-offs,7) To apply the results to conduct large-scale experiments on reading different kinds of real-world business documents, word spotting, e.g. custom's declaration forms, taxation forms, bank cheques, utility bills, and archival documents of historical value, 8) To Introduce this new breed of hybrid classifiers which can identify confusing character/word shapes, minimize costly substitution errors and maximize performance, so that the newly developed systems can be used in practice to save billions of dollars and a huge amount of manpower.9) Expand this project to recognize other types of patterns such as irises, faces, palmprints, and pedestrian patterns..

该程序的目的是在计算机识别手写字符，单词和符号方面进行高级创新研究。由于角色形状和品质无限，手写是模式识别领域中最具挑战性的主题之一。尽管手写识别系统具有阅读银行检查，信封，档案文件，公用事业付款单，所得税申报表和其他业务形式的潜力，但由于当前识别系统的高错误率，目前实际上很少使用。确实，银行和不同的公司每周花费数十亿美元，将手动数据手动输入计算机。过去，大多数研究都集中在高识别率上，而更加重视错误率的最困难和昂贵的问题。这项研究通过调查降低错误率以提高识别性的可靠性的不同方法来解决这一挑战，以在实用环境中产生新一代的识别系统，以进行全面部署。这项研究旨在提高计算机的智能和能力，以便可以高速阅读，并以各种计算机的方式来读取各种各样的文档，以便从一家票据中撰写。 Based on decades of experience plus a world renowned reputation in(a) conducting intense research in handwriting recognition,(b) having developed numerous high performance recognition systems, (c) having guided more than 190 graduate students, post-doctoral fellows, and visiting academic/industrial scientists, and(d) playing a lead role in this field and constantly interacting with prominent researchers around the world, the applicant proposes the following program to minimize the error rate and maximize识别得分：1）继续研究替换错误的原因和当前识别系统的缺点，2）探索和评估具有带有不确定性驱动模块级联的误差减少方案配置的各种配置5）5）要选择多个多阶段识别系统的各个阶段的互补特征集，以生成最可靠的和最佳系统，以识别不同语言，英语，法语，法语，阿拉伯语，中文等的手写数据，以将降低错误识别系统与高绩效系统的范围进行整合，并在范围内进行较大的范围，并发现其整体效果的范围，以实现其整体效果，从类型的现实世界文档，单词斑点，例如Custom的声明表格，税收形式，银行支票，公用事业账单和历史价值的档案文件，8）介绍这种新型的混合分类器，这些分类器可以识别令人困惑的性格/单词形状，最大程度地减少昂贵的替代性能并最大程度地提高性能并最大程度地提高性能，从而在实践中可以使用该型号的范围来节省数十亿美元的范围。棕榈印刷和行人图案..