Error Reduction in Handwriting Recognition with applications

点击翻译按钮获取中文摘要

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. .