Optical character recognition for cursive handwriting

Date

2002-06-01

Author

Arica, N
Yarman Vural, Fatoş Tunay

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In this paper, a new analytic scheme, which uses a sequence of segmentation and recognition algorithms, is proposed for offline cursive handwriting recognition problem. First, some global parameters, such as slant angle, baselines, and stroke width and height are estimated. Second, a segmentation method finds character segmentation paths by combining gray scale and binary information. Third, Hidden Markov Model (HMM) is employed for shape recognition to label and rank the character candidates. For this purpose, a string of codes is extracted from each segment to represent the character candidates. The estimation of feature space parameters is embedded in HMM training stage together with the estimation of the HMM model parameters. Finally, the lexicon information and HMM ranks are combined in a graph optimization problem for word-level recognition. This method corrects most of the errors produced by segmentation and HMM ranking stages by maximizing an information measure in an efficient graph search algorithm. The experiments in dicate higher recognition rates compared to the available methods reported in the literature.

Subject Keywords

Handwritten word recognition, Preprocessing, Segmentation, Optical character recognition, Cursive handwriting, Hidden Markov model, Search, Graph, Lexicon matching

URI

https://hdl.handle.net/11511/62516

Journal

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

DOI

https://doi.org/10.1109/tpami.2002.1008386

Collections

Department of Computer Engineering, Article

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Citation Formats

N. Arica and F. T. Yarman Vural, “Optical character recognition for cursive handwriting,” IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, pp. 801–813, 2002, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/62516.