Strategies for cursive script recognition using hidden Markov models

Gilloux, M.; Leroux, Manuel; Bertille, J.-M.

doi:10.1007/bf01219587

Cited by 19 publications

(14 citation statements)

References 27 publications

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“…All methods by Guillevic et al, [39], Saon et al, [70], and Gilloux et al, [32][33][34], represent a word model as a chain of n identical sub-HMMs (see an example in Fig. 5), where n is the most probable length of an observation sequence obtained from the training samples.…”

Section: Hmmsmentioning

confidence: 99%

Offline cursive script word recognition ? a survey

Steinherz

Rivlin

Intrator

1999

International Journal on Document Analysis and Recognition

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Abstract.We review the field of offline cursive word recognition. We mainly deal with the various methods that were proposed to realize the core of recognition in a word recognition system. These methods are discussed in view of the two most important properties of such a system: the size and nature of the lexicon involved, and whether or not a segmentation stage is present. We classify the field into three categories: segmentation-free methods, which compare a sequence of observations derived from a word image with similar references of words in the lexicon; segmentation-based methods, that look for the best match between consecutive sequences of primitive segments and letters of a possible word; and the perception-oriented approach, that relates to methods that perform a human-like reading technique, in which anchor features found all over the word are used to bootstrap a few candidates for a final evaluation phase.

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Section: Hmmsmentioning

confidence: 99%

Offline cursive script word recognition ? a survey

Steinherz

Rivlin

Intrator

1999

International Journal on Document Analysis and Recognition

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“…To simulate this problem, we have generated random lexicons, half of TABLE 1 Recognition Rates Obtained with Models w I , w P , and w Q which do not contain the correct answers. In this case, Bayes' probability becomes [5]:…”

Section: Rejectionmentioning

confidence: 99%

“…Hidden Markov models have been applied in several areas during the last 15 years, including speech recognition [9], [10], [11], language modeling [13], handwriting recognition [4], [5], [6], online signature verification [14], etc. A hidden Markov model is a doubly stochastic process, with an underlying stochastic process that is not observable (hence the word hidden), but can be observed through another stochastic process that produces the sequence of observations [11].…”

Section: Hidden Markov Modelsmentioning

confidence: 99%

“…For small lexicons, as in bank check processing, most approaches are global, where a word is considered as an indivisible entity [1], [2], [3]. For large lexicons, as in postal applications [4], [5], [6], the segmentation of words into basic units such as letters is required. Owing to the difficulty of this operation, most successful approaches are segmentation-recognition methods in which words are first loosely segmented into letters or pieces of letters, and a dynamic programming technique is used in recognition to choose the definitive segmentation [7], [8].…”

Section: Introductionmentioning

confidence: 99%

“…The success of HMMs in speech recognition has recently led many researchers to apply them to handwriting recognition by representing each word image as a sequence of observations. According to the way this representation is carried out, two approaches can be distinguished: implicit segmentation [4], [12], which leads to a speech-like representation of the handwritten word image, and explicit segmentation [5], [6], which requires a segmentation algorithm to split words into letters or pseudoletters.…”

Section: Introductionmentioning

confidence: 99%

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An HMM-based approach for off-line unconstrained handwritten word modeling and recognition

Yacoubi

Gilloux²,

Sabourin

et al. 1999

IEEE Trans. Pattern Anal. Machine Intell.

195

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AbstractÐThis paper describes a hidden Markov model-based approach designed to recognize off-line unconstrained handwritten words for large vocabularies. After preprocessing, a word image is segmented into letters or pseudoletters and represented by two feature sequences of equal length, each consisting of an alternating sequence of shape-symbols and segmentationsymbols, which are both explicitly modeled. The word model is made up of the concatenation of appropriate letter models consisting of elementary HMMs and an HMM-based interpolation technique is used to optimally combine the two feature sets. Two rejection mechanisms are considered depending on whether or not the word image is guaranteed to belong to the lexicon. Experiments carried out on real-life data show that the proposed approach can be successfully used for handwritten word recognition.

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