Optical character recognition for cursive handwriting

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.


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Arica, N; Yarman Vural, Fatoş Tunay (1998-04-23)
In this study, we introduce a new scheme for off-line handwritten connected digit string recognition problem, which uses a sequence of segmentation and recognition algorithms. The proposed system assumes no constraint in writing style, size or variations.
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A new scheme for off-line handwritten connected digit recognition
Arica, N; Yarman Vural, Fatoş Tunay (1998-08-20)
A new scheme is proposed for off-line handwritten connected digit recognition, which uses a sequence of segmentation and recognition algorithms. First, the connected digits are segmented by employing both the gray scale and binary information. Then, a new set of features is extracted from the segments. The parameters of the feature set are adjusted during the training stage of the Hidden Markov Model (HMM) where the potential digits are recognized. Finally, in order to confirm the preliminary segmentation a...
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.