STRUCTURAL RECOGNITION OF HANDWRITTEN NUMERAL STRINGS.

This thesis discusses the development of algorithms for the recognition of handwritten numeral strings in their various forms viz. isolated, broken and connected. For isolated numerals, the use of a new class of Fourier shape descriptors derived from the contours of the numeral together with a new class of topological features is shown to yield high recognition accuracy ((TURNEQ) 98%). For isolated and possibly broken numerals, a syntactic recognition algorithm that utilizes features derived from the left and right profiles of the numerals is shown to yield fast and accurate recognition. Finally, an algorithm for segmenting connected handwritten numeral strings has been developed and is shown to yield accurate segmentation. The segmented numerals are then identified by the syntactic recognition system.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis1985 .B337. Source: Dissertation Abstracts International, Volume: 46-08, Section: B, page: 2751. Thesis (Ph.D.)--University of Windsor (Canada), 1985

Handwritten Character Recognition of South Indian Scripts: A Review

Handwritten character recognition is always a frontier area of research in the field of pattern recognition and image processing and there is a large demand for OCR on hand written documents. Even though, sufficient studies have performed in foreign scripts like Chinese, Japanese and Arabic characters, only a very few work can be traced for handwritten character recognition of Indian scripts especially for the South Indian scripts. This paper provides an overview of offline handwritten character recognition in South Indian Scripts, namely Malayalam, Tamil, Kannada and Telungu.Comment: Paper presented on the "National Conference on Indian Language Computing", Kochi, February 19-20, 2011. 6 pages, 5 figure

An Efficient Hidden Markov Model for Offline Handwritten Numeral Recognition

Traditionally, the performance of ocr algorithms and systems is based on the recognition of isolated characters. When a system classifies an individual character, its output is typically a character label or a reject marker that corresponds to an unrecognized character. By comparing output labels with the correct labels, the number of correct recognition, substitution errors misrecognized characters, and rejects unrecognized characters are determined. Nowadays, although recognition of printed isolated characters is performed with high accuracy, recognition of handwritten characters still remains an open problem in the research arena. The ability to identify machine printed characters in an automated or a semi automated manner has obvious applications in numerous fields. Since creating an algorithm with a one hundred percent correct recognition rate is quite probably impossible in our world of noise and different font styles, it is important to design character recognition algorithms with these failures in mind so that when mistakes are inevitably made, they will at least be understandable and predictable to the person working with theComment: 6pages, 5 figure