Shape-Based Alphabet for Off-line Arabic Handwriting Recognition

Menasri, F.; Vincent, Nicole; Augustin, E.; Cheriet, Mohamed

doi:10.1109/icdar.2007.4377059

Cited by 32 publications

(27 citation statements)

References 2 publications

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“…These fields are active research areas. Example research in these fields are [2][3][4][5][6], respectively. Researchers dealing with processing of unconstrained handwritten Arabic cursive scripts must overcome many difficulties such as unlimited variation in human handwriting, similarities of distinct character shapes, character overlaps, and interconnections of neighboring characters.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Handwritten Arabic Letters Using Selected Feature Extraction Techniques

Abandah¹,

Khedher²

2009

Int. J. Comp. Proc. Lang.

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The Arabic letters are used in many written languages. However, little work has been done to analyze and characterize handwritten Arabic letters comprehensively. Such characterization is important for the active research in the computer processing of Arabic written scripts. We extract carefully selected features from a large database of handwritten Arabic letters, from the letter's secondary components, main body, skeleton, and boundary. These features are studied and statistically analyzed to reach the targeted characterization. Observations about the important writing style variations are presented and statistically specified. The Arabic letters have multiple forms depending on the letter's position in the word. Comparisons among the four main letter forms (isolated, initial, medial, and final) are also presented.

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Section: Introductionmentioning

confidence: 99%

“…Multiple Arabic letters that only differ in the existence and number of dots are mapped to the same root shape. This alphabet also includes common vertical ligatures of joined letters [4].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Handwritten Arabic Letters Using Selected Feature Extraction Techniques

Abandah¹,

Khedher²

2009

Int. J. Comp. Proc. Lang.

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“…All filled forms were digitized at 300 dpi and stored as binary images. The database mainly targets preprocessing [118][119][120] and recognition of Arabic handwritten words [121][122][123][124][125][126] but has also been employed to evaluate writer identification systems [127][128][129]. Figure 5 illustrates a town name from the database written by 12 different writers.…”

Section: Ifn/enitmentioning

confidence: 99%

A comprehensive survey of handwritten document benchmarks: structure, usage and evaluation

Hussain

Raza²,

Siddiqi

et al. 2015

J Image Video Proc.

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Handwriting has remained one of the most frequently occurring patterns that we come across in everyday life. Handwriting offers a number of interesting pattern classification problems including handwriting recognition, writer identification, signature verification, writer demographics classification and script recognition, etc. Research in these and similar related problems requires the availability of handwritten samples for validation of the developed techniques and algorithms. Like any other scientific domain, the handwriting recognition community has developed a large number of standard databases allowing development, evaluation and comparison of different techniques developed for a variety of recognition tasks. This paper is intended to provide a comprehensive survey of the handwriting databases developed during the last two decades. In addition to the statistics of the discussed databases, we also present a comparison of these databases on a number of dimensions. The ground truth information of the databases along with the supported tasks is also discussed. It is expected that this paper would not only allow researchers in handwriting recognition to objectively compare different databases but will also provide them the opportunity to select the most appropriate database(s) for evaluation of their developed systems.

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“…Graphemes represent more fundamental units in handwriting ranging from single strokes to actual characters [33, p. 3f]. They are used for both Roman and Arabic script recognizers (cf., e.g., [58,89,112,126]). …”

Section: Building the Writing Modelmentioning

confidence: 99%

“…In [20] and related publications, the NN-based VQ offers the advantage that it can be optimized jointly with the parameters of the HMM. In [89], the NN is used to directly compute the output probabilities of the HMM.…”

Section: Modeling Output Behaviormentioning

confidence: 99%

Markov models for offline handwriting recognition: a survey

Plötz

Fink

2009

IJDAR

190

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Since their first inception more than half a century ago, automatic reading systems have evolved substantially, thereby showing impressive performance on machine-printed text. The recognition of handwriting can, however, still be considered an open research problem due to its substantial variation in appearance. With the introduction of Markovian models to the field, a promising modeling and recognition paradigm was established for automatic offline handwriting recognition. However, so far, no standard procedures for building Markov-model-based recognizers could be established though trends toward unified approaches can be identified. It is therefore the goal of this survey to provide a comprehensive overview of the application of Markov models in the research field of offline handwriting recognition, covering both the widely used hidden Markov models and the less complex Markov-chain or n-gram models. First, we will introduce the typical architecture of a Markov-modelbased offline handwriting recognition system and make the reader familiar with the essential theoretical concepts behind Markovian models. Then, we will give a thorough review of the solutions proposed in the literature for the open problems how to apply Markov-model-based approaches to automatic offline handwriting recognition.

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Shape-Based Alphabet for Off-line Arabic Handwriting Recognition

Cited by 32 publications

References 2 publications

Analysis of Handwritten Arabic Letters Using Selected Feature Extraction Techniques

Analysis of Handwritten Arabic Letters Using Selected Feature Extraction Techniques

A comprehensive survey of handwritten document benchmarks: structure, usage and evaluation

Markov models for offline handwriting recognition: a survey

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