Recognition of Off-Line Cursive Handwriting

An entirely novel text recognition system capable of recognizing off-line handwritten Arabic cursive text having a high variability is presented [I. S. I. Abuhaiba, Ph.D. thesis, Loughborough University, 1996]. Thinned images of strokes are converted to straight-line approximations. A straight-line...

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Vydáno v:	Computer vision and image understanding Ročník 71; číslo 1; s. 19 - 38
Hlavní autoři:	Abuhaiba, I.S.I, Holt, M.J.J, Datta, S
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	San Diego, CA Elsevier Inc 01.07.1998 Elsevier
Témata:	Applied sciences Approximation theory Artificial intelligence Automata. Abstract machines. Turing machines Computer science; control theory; systems Exact sciences and technology Feature extraction Fuzzy sets Image coding Learning and adaptive systems Pattern recognition systems Pattern recognition. Digital image processing. Computational geometry Problem solving Sequential machines Theoretical computing fuzzy sequential machine temporal information assignment problem handwritten cursive text off-line recognition straight line approximation token Approximation Segmentation Image processing Data acquisition Pattern recognition Experimental study Algorithm Fuzzy logic Arabic System performance Manuscript character Learning algorithm Artificial intelligence Pattern extraction
ISSN:	1077-3142, 1090-235X
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Abstract	An entirely novel text recognition system capable of recognizing off-line handwritten Arabic cursive text having a high variability is presented [I. S. I. Abuhaiba, Ph.D. thesis, Loughborough University, 1996]. Thinned images of strokes are converted to straight-line approximations. A straight-line approximation of an off-line stroke is converted to a one-dimensional representation by a novel algorithm which aims to recover the original sequence of writing. Tokens are extracted from a one-dimensional representation of a stroke. Fuzzy sequential machines are defined to work as recognizers of tokens. The tokens of a stroke are recombined to meaningful strings of tokens. The best sets of basic shapes which represent the best sets of token strings that constitute unknown strokes are found. A method is developed to extract lines from pages of handwritten text, arrange main strokes of extracted lines in the same order as they were written, and present secondary strokes to main strokes. Presented secondary strokes are combined with basic shapes to obtain the final characters by formulating and solving assignment problems for this purpose. Some secondary strokes which remain unassigned are individually manipulated. The system was tested against the handwritings of 20 subjects yielding overall subword and character recognition rates of 55.4 and 51.1%, respectively.
AbstractList	An entirely novel text recognition system capable of recognizing off-line handwritten Arabic cursive text having a high variability is presented. Thinned images of strokes are converted to straight-line approximations. A straight-line approximation of an off-line stroke is converted to a one-dimensional representation by a novel algorithm which aims to recover the original sequence of writing. Tokens are extracted from a one-dimensional representation of a stroke. Fuzzy sequential machines are defined to work as recognizers of tokens. The tokens of a stroke are recombined to meaningful strings of tokens. The best sets of basic shapes which represent the best sets of token strings that constitute unknown strokes are found. A method is developed to extract lines from pages of handwritten text, arrange main strokes of extracted lines in the same order as they were written, and present secondary strokes to main strokes. Presented secondary strokes are combined with basic shapes to obtain the final characters by formulating and solving assignment problems for this purpose. Some secondary strokes which remain unassigned are individually manipulated. The system was tested against the handwritings of 20 subjects yielding overall sub-word and character recognition rates of 55.4 and 51.1%, respectively. An entirely novel text recognition system capable of recognizing off-line handwritten Arabic cursive text having a high variability is presented [I. S. I. Abuhaiba, Ph.D. thesis, Loughborough University, 1996]. Thinned images of strokes are converted to straight-line approximations. A straight-line approximation of an off-line stroke is converted to a one-dimensional representation by a novel algorithm which aims to recover the original sequence of writing. Tokens are extracted from a one-dimensional representation of a stroke. Fuzzy sequential machines are defined to work as recognizers of tokens. The tokens of a stroke are recombined to meaningful strings of tokens. The best sets of basic shapes which represent the best sets of token strings that constitute unknown strokes are found. A method is developed to extract lines from pages of handwritten text, arrange main strokes of extracted lines in the same order as they were written, and present secondary strokes to main strokes. Presented secondary strokes are combined with basic shapes to obtain the final characters by formulating and solving assignment problems for this purpose. Some secondary strokes which remain unassigned are individually manipulated. The system was tested against the handwritings of 20 subjects yielding overall subword and character recognition rates of 55.4 and 51.1%, respectively.
Author	Abuhaiba, I.S.I Datta, S Holt, M.J.J
Author_xml	– sequence: 1 givenname: I.S.I surname: Abuhaiba fullname: Abuhaiba, I.S.I organization: P.O. Box 89591, Riyadh, 11692, Saudi Arabia – sequence: 2 givenname: M.J.J surname: Holt fullname: Holt, M.J.J organization: Department of Electronic and Electrical Engineering, Loughborough University, Loughborough, Leicestershire, LE11 3TU, United Kingdom – sequence: 3 givenname: S surname: Datta fullname: Datta, S organization: Department of Electronic and Electrical Engineering, Loughborough University, Loughborough, Leicestershire, LE11 3TU, United Kingdom
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Keywords	fuzzy sequential machine temporal information assignment problem handwritten cursive text off-line recognition straight line approximation token Approximation Segmentation Image processing Data acquisition Pattern recognition Experimental study Algorithm Fuzzy logic Arabic System performance Manuscript character Learning algorithm Artificial intelligence Pattern extraction
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Snippet	An entirely novel text recognition system capable of recognizing off-line handwritten Arabic cursive text having a high variability is presented [I. S. I.... An entirely novel text recognition system capable of recognizing off-line handwritten Arabic cursive text having a high variability is presented. Thinned...
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SubjectTerms	Applied sciences Approximation theory Artificial intelligence Automata. Abstract machines. Turing machines Computer science; control theory; systems Exact sciences and technology Feature extraction Fuzzy sets Image coding Learning and adaptive systems Pattern recognition systems Pattern recognition. Digital image processing. Computational geometry Problem solving Sequential machines Theoretical computing
Title	Recognition of Off-Line Cursive Handwriting
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