Coupling observation/letter for a Markovian modelisation applied to the recognition of Arabic handwriting

Miled, H.; Olivier, Christian; Cheriet, Mohamed; Lecoutier, Y.

doi:10.1109/icdar.1997.620568

Cited by 20 publications

(9 citation statements)

References 11 publications

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“…The second uses a more sophisticated explicit segmentation technique [12,13] to cut the words into more meaningful units or graphemes, which are larger than the frames. Our approach belongs to the second one.…”

Section: Application and Experimental Resultsmentioning

confidence: 99%

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Recognition of Handwritten Arabic Literal Amounts Using a Hybrid Approach

Boukharouba

Bennia

2010

Cogn Comput

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This paper describes a new approach to combine a multilayer perceptron (MLP) and a hidden Markov model for recognizing handwritten Arabic words. As a first step, connected components (CCs) of black pixels are detected, then the system determines which CCs are subwords and which are diacritics. The diacritics are then isolated and identified separately, and the sub-words are segmented into graphemes. The MLP is used as labeller (classifier) and probability estimator. We also introduce the diacritics and their positions in our hybrid system; thus, only one model including both grapheme and diacritic states is built to represent the whole alphabet. Finally, we consider a maximum likelihood classifier to decide about the word class. The experiments that were performed show promising results on Arabic word segmentation and recognition.

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Section: Application and Experimental Resultsmentioning

confidence: 99%

“…Finally, the word recognition is based on the tree representation lexicon and the Viterbi algorithm. Miled et al [12] adopted an HMM model for Arabic words recognition using an explicit segmentation of words into graphemes. They used a K-NN classifier to assign to each grapheme an observation.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Handwritten Arabic Literal Amounts Using a Hybrid Approach

Boukharouba

Bennia

2010

Cogn Comput

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“…El-Hajj [6] have used Neural Networks to combine three homogeneous HMM-based classifiers, which have different features as input, they used the IFN/ENIT database achieving a recognition rate of 94,44%. In [15], a strategy for Arabic handwritten word recognition has been proposed by Miled, the idea is based on a sequential hierarchical cooperation of three classifiers, all of a Markovian type. The first classifier is based on a global description of the word using sequential visual indices.…”

Section: Related Workmentioning

confidence: 99%

“…As mentioned in the abstract, we compared the developed system to three other systems tested also on IFN/EINIT database; the ones of El-hajj [6], Miled [15] and Burrow [2], it may be noted from table 2 that the highest accuracy was obtained by El-Hajj, this is due to the use of a segmentation phase, on the other side our system achieves a good accuracy compared with Burrow's and Miled's system which prove the performance of combining an RBF and MLP with a Fuzzy ART network. …”

Section: Comparison With Other Systemsmentioning

confidence: 99%

Combining neural networks for Arabic handwriting recognition

Chergui

Kef

Chikhi

2011

2011 10th International Symposium on Programming and Systems

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“…At this level, each grapheme is represented by a vector of the different measurements used by the classifier to compare an unknown grapheme to known ones. The decision of the observation given to each grapheme is made using a k nearest neighbors classifier (k-NN) [16].…”

Section: Observations Vs Graphemesmentioning

confidence: 99%

Multi-level arabic handwritten words recognition

Miled

Cheriet

Olivier

1998

Lecture Notes in Computer Science

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In this paper, we present a strategy of Arabic words recognition by combining two levels which are based on global and analytical approaches according to the topological properties of Arabic handwriting. In the first level (global), we consider the visual indices which can be generated by: diacritics and strokes (denoted tracing) that form the main shapes of the word. Each word is described as a sequence of visual indices which is treated by a "global" classifier based on Hidden Markov Model (HMM). In the second level, the word is segmented into graphemes, then each grapheme is transformed into a HMM observation by a vector quantization phase. An analytical HMM is developed in order to manage the observation sequences. At this level the diacritics are not taken in consideration which allows to reduce the number of estimated character models. Finally we combine the two approaches to decide on the class of an unknown word. In fact, the global model serves as a filter. It produces a set of hypotheses to the analytical model, which in turns, defines and outputs the final decision.

show abstract

Coupling observation/letter for a Markovian modelisation applied to the recognition of Arabic handwriting

Cited by 20 publications

References 11 publications

Recognition of Handwritten Arabic Literal Amounts Using a Hybrid Approach

Recognition of Handwritten Arabic Literal Amounts Using a Hybrid Approach

Combining neural networks for Arabic handwriting recognition

Multi-level arabic handwritten words recognition

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