An improved algorithm for thinning binary digital patterns

Nagendraprasad, M.V.; Wang, P.S.P.; Gupta, Amar

doi:10.1109/icpr.1992.202005

Cited by 7 publications

(1 citation statement)

References 4 publications

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“…Detailed description can be found in [17] [18], [19] and [20]. Neural Classifier: This stage classifies the bitmap into one of the ten classes and is discussed in detail in Section 3.…”

Section: Syntax Checker and Processingmentioning

confidence: 99%

An Adaptive Modular Neural Network With Application to Unconstrained Character Recognition

Mui¹

1994

Document Image Analysis

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“…Detailed description can be found in [17] [18], [19] and [20]. Neural Classifier: This stage classifies the bitmap into one of the ten classes and is discussed in detail in Section 3.…”

Section: Syntax Checker and Processingmentioning

confidence: 99%

An Adaptive Modular Neural Network With Application to Unconstrained Character Recognition

Mui¹

1994

Document Image Analysis

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Parallel thinning algorithm based on the wave propagation's model

Xia

1995

Computer Analysis of Images and Patterns

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A knowledge-based segmentation algorithm for enhanced recognition of handwritten courtesy amounts

Hussein¹,

Agarwal²,

Gupta³

et al. 1999

Pattern Recognition

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The Productivity From Information Technology (PROFIT) Initiative was established on October 23, 1992 by MIT President Charles Vest and Provost Mark Wrighton "to study the use of information technology in both the private and public sectors and to enhance productivity in areas ranging from finance to transportation, and from manufacturing to telecommunications." At the time of its inception, PROFIT took over the Composite Information Systems Laboratory and Handwritten Character Recognition Laboratory. These two laboratories are now involved in research related to context mediation and imaging respectively. OR[RING In addition, PROFIT has undertaken joint efforts with a number of research centers, laboratories, and programs at MIT, and the results of these efforts are documented in Discussion Papers published by PROFIT and/or the collaborating MIT entity.Correspondence can be addressed to:The Researchers at MIT have looked at the possibility of taking information directly from paper documents, especially handwritten documents, to computeraccessible media. Automated reading involves several steps as follows:Scanning of document; Location of area to be "read"; Decomposing the selected area into separate characters; Adjusting size and slant of each character; Recognizing each character; and Testing whether input has been correctly read.Based on several years of sustained research, the researchers have attained very high "reading" speed and accuracy, even in situations where the quality of the input material is poor. Patent rights for some of the new techniques have been applied for. Sponsor companies are eligible to test the new techniques in their respective environments at no charge.The work performed so far is described in a number of working papers. AbstractA knowledge based segmentation critic algorithm to enhance recognition of courtesy amounts on bank checks is proposed in this paper. This algorithm extracts the context from the handwritten material and uses a syntax parser based on a deterministic finite automaton to provide adequate feedback to enhance recognition. The segmentation critic presented is capable of handling a number of commonly used styles for courtesy amount representation. Both handwritten and machine written numeric strings were utilized to test the efficacy of the preprocessor for the check recognition system described in this paper. The substitution error fell by 1.0% in our early tests.

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An improved algorithm for thinning binary digital patterns

Cited by 7 publications

References 4 publications

An Adaptive Modular Neural Network With Application to Unconstrained Character Recognition

An Adaptive Modular Neural Network With Application to Unconstrained Character Recognition

Parallel thinning algorithm based on the wave propagation's model

A knowledge-based segmentation algorithm for enhanced recognition of handwritten courtesy amounts

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