Optical Character Recognition for Cursive Handwriting

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Optical Character Recognition for Cursive Handwriting
Nafiz Arica, Student Member, IEEE, and Fatos T. Yarman-Vural, Senior Member, IEEE AbstractÐIn 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. Index TermsÐHandwritten word recognition, preprocessing, segmentation, optical character recognition, cursive handwriting, hidden Markov model, search, graph, lexicon matching.

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HE most difficult problem in the field of Optical Character Recognition (OCR) is the recognition of unconstrained cursive handwriting. The present tools for modeling almost infinitely many variations of human handwriting are not yet sufficient. The similarities of distinct character shapes, the overlaps, and interconnection of the neighboring characters further complicate the problem. Additionally, when observed in isolation, characters are often ambiguous and require context information to reduce the classification error. Thus, current research aims at developing constrained systems for limited domain applications such as postal address reading [21], check sorting [8], tax reading [20], and office automation for text entry [7]. A well-defined lexicon plus a well-constrained syntax help provide a feasible solution to the problem [11]. Handwritten Word Recognition techniques use either holistic or analytic strategies for training and recognition stages. Holistic strategies employ top-down approaches for recognizing the whole word, thus eliminating the segmentation problem [9]. In this strategy, global features, extracted from the entire word image, are used in recognition of limited-size lexicon. As the size of the lexicon gets larger, the complexity of algorithms increase linearly due to the need for a larger search space and a more complex pattern representation. Additionally, the recognition rates decrease rapidly due to the decrease in betweenclass-variances in the feature space. The analytic strategies, on the other hand, employ bottom-up approaches, starting from stroke or character-

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INTRODUCTION
level and going towards producing a meaningful text. Explicit [23] or implicit [16] segmentation of word into characters or strokes is required for this strategy. With the cooperation of segmentation stage, the problem is reduced to the recognition of simple isolated characters or strokes, which can be handled for unlimited vocabulary. However, there is no segmentation algorithm available in the literature for correctly extracting the characters from a given word image. The popular techniques are based on over-segmenting the words and applying a search algorithm for grouping segments to make up characters [14], [10]. If a lexicon of limited size is given, dynamic programming is used to rank every word in the lexicon. The word with the highest rank is chosen as the recognition hypothesis. The complexity of search process for this strategy also increases linearly with the...
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