A two-stage HMM-based system for recognizing handwritten numeral strings

Britto, Alceu S.; Sabourin, Robert; Bortolozzi, Flávio; Suen, C.Y.

doi:10.1109/icdar.2001.953820

Cited by 19 publications

(14 citation statements)

References 8 publications

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“…Since the performance of a learning algorithm, as stated by the no free-lunch theorem [1], is strictly dependent on the problem to which it is applied, the main goal of this paper is to provide an evaluation of IL algorithms in the recognition of isolated handwritten characters, by considering a state-of-the-art hidden Markov model (HMM)-based handwriting recognition system [8][9][10]. An HMM-based framework has been selected due to the potential of HMMs for the handwriting recognition problem in general, and the application field of IL algorithms for this kind of system is vast.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of incremental learning algorithms for HMM in the recognition of alphanumeric characters

Cavalin

Sabourin

Suen

et al. 2009

Pattern Recognition

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Section: Introductionmentioning

confidence: 99%

Evaluation of incremental learning algorithms for HMM in the recognition of alphanumeric characters

Cavalin

Sabourin

Suen

et al. 2009

Pattern Recognition

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“…Based on an error analysis, verification by linear tournament with one-to-one verifiers between two categories was proposed and such a verification scheme increased the recognition rate by 1.2 percent. Britto et al [21] used a verification stage to enhance the recognition of a handwritten numeral string HMM-based system. The verification stage, composed of 20 numeral HMMs, has improved the recognition rate for strings of different lengths by about 10 percent (from 81.65 percent to 91.57 percent).…”

Section: Introductionmentioning

confidence: 99%

Recognition and verification of unconstrained handwritten words

Koerich

Sabourin

Suen

2005

IEEE Trans. Pattern Anal. Machine Intell.

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Abstract-This paper presents a novel approach for the verification of the word hypotheses generated by a large vocabulary, offline handwritten word recognition system. Given a word image, the recognition system produces a ranked list of the N-best recognition hypotheses consisting of text transcripts, segmentation boundaries of the word hypotheses into characters, and recognition scores. The verification consists of an estimation of the probability of each segment representing a known class of character. Then, character probabilities are combined to produce word confidence scores which are further integrated with the recognition scores produced by the recognition system. The N-best recognition hypothesis list is reranked based on such composite scores. In the end, rejection rules are invoked to either accept the best recognition hypothesis of such a list or to reject the input word image. The use of the verification approach has improved the word recognition rate as well as the reliability of the recognition system, while not causing significant delays in the recognition process. Our approach is described in detail and the experimental results on a large database of unconstrained handwritten words extracted from postal envelopes are presented.

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“…Although certain publications regarding Markov-modelbased recognition of isolated characters exist (cf., e.g., [21,56,57,95]), it is at least questionable whether the use of these models is appropriate for such data. Instead, the approach shows its strength especially for sequences.…”

Section: Applicationsmentioning

confidence: 99%

“…As, for example, described in [21,121,122], MM-based HWR systems have been developed that combine the results of at least two consecutive recognition stages.…”

Section: Multi-classifier Combinationmentioning

confidence: 99%

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Markov models for offline handwriting recognition: a survey

Plötz

Fink

2009

IJDAR

191

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Since their first inception more than half a century ago, automatic reading systems have evolved substantially, thereby showing impressive performance on machine-printed text. The recognition of handwriting can, however, still be considered an open research problem due to its substantial variation in appearance. With the introduction of Markovian models to the field, a promising modeling and recognition paradigm was established for automatic offline handwriting recognition. However, so far, no standard procedures for building Markov-model-based recognizers could be established though trends toward unified approaches can be identified. It is therefore the goal of this survey to provide a comprehensive overview of the application of Markov models in the research field of offline handwriting recognition, covering both the widely used hidden Markov models and the less complex Markov-chain or n-gram models. First, we will introduce the typical architecture of a Markov-modelbased offline handwriting recognition system and make the reader familiar with the essential theoretical concepts behind Markovian models. Then, we will give a thorough review of the solutions proposed in the literature for the open problems how to apply Markov-model-based approaches to automatic offline handwriting recognition.

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A two-stage HMM-based system for recognizing handwritten numeral strings

Cited by 19 publications

References 8 publications

Evaluation of incremental learning algorithms for HMM in the recognition of alphanumeric characters

Evaluation of incremental learning algorithms for HMM in the recognition of alphanumeric characters

Recognition and verification of unconstrained handwritten words

Markov models for offline handwriting recognition: a survey

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