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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Shrnutí: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.
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ISSN:1077-3142
1090-235X
DOI:10.1006/cviu.1997.0629