&lt;title&gt;Recognition of Arabic handwritten words using contextual character models&lt;/title&gt;

El-Hajj, Ramy; Mokbel, Chafic; Likforman-Sulem, Laurence

doi:10.1117/12.765868

Cited by 9 publications

(4 citation statements)

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“…As can be seen from the table, the performance could be increased significantly beyond the baseline. El-Hajj [12] evaluated the performance of his recognition system on the benchmark database IFN-ENIT. Results in table shown an improvement due to the contextual character modeling: accuracy is increased by 0.6% in absolute value which corresponds to a 7.8% reduction in error rate; this shows the effectiveness of modeling overlapped characters by specific models while keeping the total number of models manageable.…”

Section: Comparative Resultsmentioning

confidence: 99%

“…The probability densities of observations in each state are modeled as a mixture of three Gaussian distributions. El-Hajj [12] simplify the tri-character approach and present a parallel alternative in which select manually the characters which are overlapped by neighboring strokes and construct the corresponding contextual model. This leads to a lower number of additional models than with the tri-character approach because characters are modeled by at most two models.…”

Section: Ramy El-hajj [12]mentioning

confidence: 99%

“…Feature extraction is based on the work of El-Hajj et al [12]. Context-independent system uses the analytical strategy and is segmentation-free.…”

Section: Context Independent Handwriting Recognition Systemmentioning

confidence: 99%

See 2 more Smart Citations

A Survey of Contextual Handwritten Recognition Systems based HMMs for Cursive Arabic and Latin Script

Rabi¹,

Amrouch²,

Mahani³

2017

IJCA

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Offline handwriting recognition has become lately a very popular research area and the number of its possible application is very large. Most recognition system are based on modeling characters to recognize, then the concatenation of these models to recognize a word, while modeling character allows deformations related to its context. This paper provides a survey of handwritten recognition systems based on context-dependent character modeling to account possible deformations related to its context. It examines the literature on the most significant work in contextual handwritten text recognition for two different alphabets, Latin and Arabic. Finally discussing the comparative results to achieve a comprehensive summary of the various approaches and systems taking account the character's context which could help open up some interesting new prospects.

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Section: Comparative Resultsmentioning

confidence: 99%

Section: Ramy El-hajj [12]mentioning

confidence: 99%

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A Survey of Contextual Handwritten Recognition Systems based HMMs for Cursive Arabic and Latin Script

Rabi¹,

Amrouch²,

Mahani³

2017

IJCA

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“…Analysis of Arabic documents and recognition/indexing of Arabic text actually became an attracting research domain in the recent years [4,5,6,7]. Major contributions have already been made in the field of printed and handwritten Arabic text data sets and OCR systems [4,6,8,9,10,11].…”

Section: Introductionmentioning

confidence: 99%

Semi-automatic news video annotation framework for Arabic text

Zayene

Touj

Hennebert

et al. 2014

2014 4th International Conference on Image Processing Theory, Tools and Applications (IPTA)

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In this paper, we present a semi-automatic news video annotation tool. The tool and its algorithms are dedicated to artificial Arabic text embedded in video news in the form of static text as well as scrolling one. It is performed at two different levels. Including specificities of Arabic script, the tool manages a global level which concerns the entire video and a local level which concerns any specific frame extracted from the video. The global annotation is performed manually thanks to a user interface. As a result of this step, we obtain the global xml file. The local annotation at the frame level is done automatically according to the information contained in the global metafile and a proposed text tracking algorithm. The main application of our tool is the ground truthing of textual information in video content. It is being used for this purpose in the Arabic Text in Video (AcTiV) database project in our lab. One of the functions that AcTiV provides, is a benchmark to compare existing and future Arabic video OCR systems.

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Arabic handwriting recognition using structural and syntactic pattern attributes

Parvez

Mahmoud

2013

Pattern Recognition

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<title>Recognition of Arabic handwritten words using contextual character models</title>

Cited by 9 publications

References 0 publications

A Survey of Contextual Handwritten Recognition Systems based HMMs for Cursive Arabic and Latin Script

A Survey of Contextual Handwritten Recognition Systems based HMMs for Cursive Arabic and Latin Script

Semi-automatic news video annotation framework for Arabic text

Arabic handwriting recognition using structural and syntactic pattern attributes

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