Planar Markov modeling for Arabic writing recognition: advancement state

In this paper, an improvement of a 2D stochastic model based handwritten entity recognition system is described. To model the handwriting considered as being a two dimensional signal, a context based, segmentation-free Hidden Markov Model (HMM) recognition system was used. The baseline approach combines a Markov Random Field (MRF) and a HMM so-called Non-Symmetric Half Plane Hidden Markov Model (NSHP-HMM). To improve the results performed by this baseline system operating just on low-level pixel information an extension of the NSHP-HMM is proposed. The mechanism allows to extend the observations of the NSHP-HMM by implanting structural information in the system. At present, the accuracy of the system on the SRTP1 French postal check database is 87.52% while for the handwritten Bangla city names is 86.80%. The gain using this structural information for the SRTP dataset is 1.57%.

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“…The literature proposes different methods like PHMMs [4,7] or totally 2D models using MRF as described in [2,5,11].…”

Section: Service De Recherche Technique De La Postementioning

confidence: 99%

Structural information implant in a context based segmentation-free HMM handwritten word recognition system for Latin and Bangla script

Vajda

Belaïd

2005

Eighth International Conference on Document Analysis and Recognition (ICDAR'05)

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“…This results in pseudo 2D-HMMs which are successfully implemented to recognise typewritten characters (Agazzi and Kuo, 1993) and handwritten characters (Park and Lee, 1998). 2DHMM was also implemented to recognise typewritten and handwritten Arabic words (Miled and Amra, 2001). Abdelazim and Hashish (1989) applied HMMs to recognise Hindu numerals that are used with Arabic text; ' -0123456789'.…”

Section: Introductionmentioning

confidence: 99%

Offline recognition of omnifont Arabic text using the HMM ToolKit (HTK)

Khorsheed

2007

Pattern Recognition Letters

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“…Hybrid approach is the combination of the 2 approaches [4]. In [10] pseudo-analytical approaches are proposed with exploiting inherent concept of Arabic script, which is pseudo-word. This concept is generated by the presence of six letters in Arabic alphabet that cannot be related to a successor.…”

Section: Introductionmentioning

confidence: 99%

Affixal approach for Arabic decomposable vocabulary recognition a validation on printed word in only one font

Kanoun

Alimi

Lecourtier

2005

Eighth International Conference on Document Analysis and Recognition (ICDAR'05)

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We propose a new approach for Arabic word recognition called affixal approach. This approach is founded on morphological structure of Arabic vocabulary. A mechanism of decompositionrecognition is used in our approach and makes it possible to lead to a set of reliable solutions for each word. This mechanism tries to recognize word basic morphemes: prefix, infix, suffix and root contrary to existing approaches which are usually based on recognition of word entity by holistic approach, pseudo-word entity by pseudo-analytical approach or letter entity by analytical approach. In this paper, we will present limits of existing approaches for Arabic word recognition. We will expose then Arabic vocabulary structure. We will detail after affixal approach for Arabic decomposable vocabulary recognition with a word example. Lastly, we will expose experimental results obtained on a basis of 1000 words data set.

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Planar Markov modeling for Arabic writing recognition: advancement state

Cited by 23 publications

References 6 publications

Structural information implant in a context based segmentation-free HMM handwritten word recognition system for Latin and Bangla script

Structural information implant in a context based segmentation-free HMM handwritten word recognition system for Latin and Bangla script

Offline recognition of omnifont Arabic text using the HMM ToolKit (HTK)

Affixal approach for Arabic decomposable vocabulary recognition a validation on printed word in only one font

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