Handwritten Farsi (Arabic) word recognition: a holistic approach using discrete HMM

Dehghan, Mehdi; Faez, Karim; Ahmadi, Majid; Shridhar, M.

doi:10.1016/s0031-3203(00)00051-0

Cited by 128 publications

(70 citation statements)

References 9 publications

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“…The paper has also proposed an algorithm for treating the diacritical marks to recognize both single character and cursive words and has been based on state-of-the-art technologies terms [34]. In this case, a researcher has discussed a method using state of the art in their paper titled ''State-of-the-art in Farsi Script Classification'' terms [11]. A data-driven systematic method has been designed using hidden Markov model (HMM) topology as a statistical model which has been concentrated for representing samples data in a single pattern class, i.e.…”

Section: Classificationmentioning

confidence: 99%

Online versus offline Arabic script classification

Saba

Almazyad

Rehman

2015

Neural Comput & Applic

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Arabic script classification is a complex area of research in the field of computer vision. The issue of offline Arabic script classification has been a concern of many researchers interest currently as it is assumed that online Arabic script recognition is comparatively simple and significant achievements have been attained. Numerous researchers deal with these issues evolved in pre-processing and post-processing techniques of Arabic script and presented various approaches to improve its accuracy rate. However, offline Arabic script classification and its related issues are still fresh. In this paper, we focus on pre-processing to post-processing techniques and highlight several issues in each phase in order to highlight need of high classification performance for Arabic script classification (offline and online). Additionally, top experimental results are reported, discussed and compared, and current challenges are also discussed. Finally, online versus offline Arabic script recognition achievements are also compared.

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Section: Classificationmentioning

confidence: 99%

Online versus offline Arabic script classification

Saba

Almazyad

Rehman

2015

Neural Comput & Applic

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“…Individual HMMs were used to create and reproduce split-feature vectors from; the contours of the horizontal and vertical projections were compliant with two HMMs per character. Through recognition, individual categories were incorporated to improve performance (Dehghan, 2001;Lorigo and Govindaraju, 2006).…”

Section: Contour Tracingmentioning

confidence: 99%

Applying Genetic Algorithm in Multi Language's Characters Recognition

Aljuaid¹

2012

Genetic Algorithms in Applications

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“…Hidden Markov Models (HMMs) are now widely used for off-line text recognition in many languages and, in particular, languages with Arabic scripts [1,[5][6][7]4]. Following the conventional approach in speech recognition [9], HMMs at global (line or word) level are built from shared, embedded HMMs at character (subword) level, which are usually simple in terms of number of states and topology.…”

Section: Introductionmentioning

confidence: 99%

Arabic Printed Word Recognition Using Windowed Bernoulli HMMs

Khoury

Giménez

Juan

et al. 2013

Image Analysis and Processing – ICIAP 2013

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Abstract. Hidden Markov Models (HMMs) are now widely used for off-line text recognition in many languages and, in particular, Arabic. In previous work, we proposed to directly use columns of raw, binary image pixels, which are directly fed into embedded Bernoulli (mixture) HMMs, that is, embedded HMMs in which the emission probabilities are modeled with Bernoulli mixtures. The idea was to by-pass feature extraction and to ensure that no discriminative information is filtered out during feature extraction, which in some sense is integrated into the recognition model. More recently, we extended the column bit vectors by means of a sliding window of adequate width to better capture image context at each horizontal position of the word image. However, these models might have limited capability to properly model vertical image distortions. In this paper, we have considered three methods of window repositioning after window extraction to overcome this limitation. Each sliding window is translated (repositioned) to align its center to the center of mass. Using this approach, state-of-art results are reported on the Arabic Printed Text Recognition (APTI) database.

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Handwritten Farsi (Arabic) word recognition: a holistic approach using discrete HMM

Cited by 128 publications

References 9 publications

Online versus offline Arabic script classification

Online versus offline Arabic script classification

Applying Genetic Algorithm in Multi Language's Characters Recognition

Arabic Printed Word Recognition Using Windowed Bernoulli HMMs

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