Ensembles of Classifiers for Handwritten Word Recognition Specialized on Individual Handwriting Style

Günter, Simon; Bunke, Horst

doi:10.1007/978-3-540-28640-0_27

Cited by 4 publications

(4 citation statements)

References 27 publications

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“…Günter and Bunke [16] combined three classifiers: HMM, ANN and K-NN with different architectures for handwritten word recognition. They use several base classifiers, rather than just a single one, to derive an ensemble.…”

Section: Related Workmentioning

confidence: 99%

Yoruba Handwriting Word Recognition Quality Evaluation of Preprocessing Attributes using Information Theory Approach

F.Ajao¹,

O.Olabiyisi²,

O.Omidiora³

et al. 2015

IJAIS

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This paper presents an approach to evaluate the quality of handwritten words using the set of features in the preprocessing stages. This is to determine the effect of various stages of preprocessing of the recognition of Yoruba handwritten characters. We demonstrate our methods using handwritten words samples of the domain of Yoruba medical terminology collected from indigenous Yoruba literate writers. Samples were digitized at 300dpi to facilitate much detail representation of the image dataset. We study the impact of entropy measure for an intuitive interpretation of the analysis of the handwritten words. From the experiment carried out, it was observed that the entropy measure of handwritten word is higher than the typewritten word. This implies that the Information content of the handwritten word is affected by perturbations which need to be removed using appropriate image preprocessing tools to obtain low entropy measure which implies same information content as the original. General TermsPattern Recognition, Handwritten word recognition, Image preprocessing and Information Theory.

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Section: Related Workmentioning

confidence: 99%

Yoruba Handwriting Word Recognition Quality Evaluation of Preprocessing Attributes using Information Theory Approach

F.Ajao¹,

O.Olabiyisi²,

O.Omidiora³

et al. 2015

IJAIS

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“…In order to achieve good discriminative power with such low level features, we attempt to combine multiple feature streams. Various combination strategies have been proposed in the literature (Günter and Bunke, 2003). They can be grouped into two broad categories: feature fusion methods and decision fusion techniques.…”

Section: Introductionmentioning

confidence: 99%

Off-line handwritten word recognition using multi-stream hidden Markov models

Kessentini

Paquet

Hamadou

2010

Pattern Recognition Letters

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To cite this version:Yousri Kessentini, Thierry Paquet, Abdelmajid Benhamadou. Off-line handwritten word recognition using multi-stream hidden Markov models. Pattern Recognition Letters, Elsevier, 2010, 31 (1) AbstractIn this paper, we present a multi-stream approach for off-line handwritten word recognition. Using 2-stream approach, the best recognition performance is 79.8%, in the case of the Arabic script, on a 2100-word lexicon consisting of 946 Tunisian town/village names. In the case of the Latin script, the proposed approach achieves a recognition rate of 89.8 % using a lexicon of 196 words.2 KeywordsOff-Line handwriting recognition, Hidden Markov Models, Latin script, Arabic script, multistream, information combination.

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“…This issue was recently addressed by the authors in a few papers [6,7,[9][10][11]. In [6] ensemble methods using several prototypes were introduced.…”

Section: Introductionmentioning

confidence: 99%

“…Ensemble methods using feature selection algorithms were introduced in [9] while a special combination scheme was described in [10]. Results of classical ensemble methods were reported in [11]. In the present paper we introduce a novel ensemble method and compare the most promising ensemble methods among each other, using a uniform framework for experimental evaluation.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Classical and Novel Ensemble Methods for Handwritten Word Recognition

Günter

Bunke

2004

Lecture Notes in Computer Science

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Abstract.Handwritten text recognition is one of the most difficult problems in the field of pattern recognition. Recently, a number of classifier creation and combination methods, known as ensemble methods, have been proposed in the field of machine learning. They have shown improved recognition performance over single classifiers. In this paper a number of ensemble methods for handwritten word recognition are described, experimentally evaluated, and compared to each other. Those methods include classical, general purpose ensemble methods as well as novel ensemble methods specifically developed by the authors for handwritten word recognition. The aim of the paper is to investigate the potential of ensemble methods for improving the performance of handwriting recognition systems. The base recognition systems used in this paper are hidden Markov model classifiers.

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Ensembles of Classifiers for Handwritten Word Recognition Specialized on Individual Handwriting Style

Cited by 4 publications

References 27 publications

Yoruba Handwriting Word Recognition Quality Evaluation of Preprocessing Attributes using Information Theory Approach

Yoruba Handwriting Word Recognition Quality Evaluation of Preprocessing Attributes using Information Theory Approach

Off-line handwritten word recognition using multi-stream hidden Markov models

Evaluation of Classical and Novel Ensemble Methods for Handwritten Word Recognition

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