Probabilistic Artificial Neural Network For Recognizing the Arabic Hand Written Characters

Khatatneh, Khalaf; Emary, Ibrahiem M. M. El; Rifai, Basem Al

doi:10.3844/jcssp.2006.879.884

Cited by 15 publications

(12 citation statements)

References 9 publications

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“…The sequential iterative thinning algorithm is the most common but should be supported with a set of procedures to preserve the text connectivity. Many researches in ACR have been carried out based on thinning, but unfortunately the used method has not been mentioned by the researchers, because they used the thinning as a preprocessing method to improve the over recognition ratio, such as Zidouri [71], Khatatneh [37] and Mozaffari [47]. Finally, The effective thinning algorithm must preserve each of the dots and text connectivity, it also does not produce spurious tails, and it must be robust to noise, as well as it avoids the necking problem.…”

Section: Discussionmentioning

confidence: 99%

The Thinning Problem in Arabic Text Recognition A Comprehensive Review

AL-Shatnawi¹,

Omar²

2014

IJCA

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The goal of this paper is to present an overview about the thinning problem in Arabic text recognition. Thinning "Skeletonization" is a very crucial stage in the ACR, it simplifies the text shape and reduces the amount of data that needs to be handled and it is usually used as a pre-processing stage for recognition and storage systems. The skeleton of Arabic text can be used for each of the baseline detection, character segmentation, and features extraction and also ultimately supporting the classification. Choosing or designing the effective thinning algorithm for Arabic text is crucial in ACR. In this paper, the importances of the thinning for the ACR and the usage of the text skeleton in ACR system are discussed and presented. As well as the challenges that have an impact on the thinning of Arabic text are discussed. The methods of Arabic text thinning are discussed and reviewed based on the technique used, and the methods advantages and drawbacks are discussed in details.

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Section: Discussionmentioning

confidence: 99%

The Thinning Problem in Arabic Text Recognition A Comprehensive Review

AL-Shatnawi¹,

Omar²

2014

IJCA

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“…The evaluation of the word recognition system was carried out on different lexicon sizes such as 1K, 2K, 5K, 10K and 20K to assess the performance. The maximum likelihood parameter estimation for HMM is obtained by the iterative procedure, the Baum-Welch algorithm [36], with multiple observation sequences.…”

Section: Statistical Techniquesmentioning

confidence: 99%

Techniques and Methodologies for Recognition of Tamil Typewritten and Handwritten Characters: A Survey

Sumathi¹,

Karpagavalli²,

Vaishnav³

2012

IJCSES

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Recording information both typewritten and handwritten has always been a common mode of communication and also for documenting purposes in day to day life. Machine recognizing of information both, type and hand written has a good practical significance in various context like, reading notes on electronic devices like Tablets, in scanned documents, like Bank cheques etc. The paper is about various methodologies and techniques that were proposed to recognize Tamil characters over time from scanned documents and other similar medium.

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“…It could assist recognition for the primary stroke classifier by excluding letters which did not have the classified secondary stroke. A method for the recognition of Arabic Text using Artificial Neural Networks in which Drift Correction is employed to overcome the problem of skewed images was presented [4]. A 4-level segmentation process is needed to segment the image to character.…”

Section: Introductionmentioning

confidence: 99%

Handwritten Arabic word recognition: A review of common approaches

Khalifa

Hassan

2008

2008 International Conference on Computer and Communication Engineering

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Automated methods for the recognition of Arabic script are at an early stage compared to their equivalent for the recognition of Latin and Chinese. In this paper, different approaches used for handwritten Arabic word recognition were reviewed. An introduction to the Arabic script is given, followed by a description of algorithms for the process involved: segmentation, feature extraction, classification, and recognition. However, an automatic recognition of text on scanned images has enabled many applications such as words spotting in large volumes of documents, automatic sorting of postal mail, and convenient editing of previously printed documents. The domain of handwriting in the Arabic script presents unique technical challenges and has been addressed more recently than other domains. Many different methods have been proposed and applied to various types of images, and a variety of approaches have been already developed, tested and returned good results. Yet the database used was too small compared to the huge size of Arabic texts, which gives way to other approaches to be developed based on a bigger database. Since handwriting recognition is such a large subject, there is plenty of scope for the work in this field. Finally, a comparison to show pros and cons of the approaches reviewed is conducted.

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Probabilistic Artificial Neural Network For Recognizing the Arabic Hand Written Characters

Cited by 15 publications

References 9 publications

The Thinning Problem in Arabic Text Recognition A Comprehensive Review

The Thinning Problem in Arabic Text Recognition A Comprehensive Review

Techniques and Methodologies for Recognition of Tamil Typewritten and Handwritten Characters: A Survey

Handwritten Arabic word recognition: A review of common approaches

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