Baseline estimation for Arabic handwritten words

Pechwitz, Mario; Märgner, Volker

doi:10.1109/iwfhr.2002.1030956

Cited by 77 publications

(51 citation statements)

References 12 publications

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“…For example, the word "Arabic" (��) has two PAWs: the first PAW consists of initial Ain ‫)ﻋ(‬ and final, left-disconnecting, Reh ( ‫;)ﺮ‬ and the second PAW consists of initial Beh ‫,)ﺑ(‬ medial Yeh ‫,)ﻴ(‬ and final Teh Marbuta ‫. )ﺔ(‬ Note that the letters are usually connected at a certain horizontal level called the baseline [25]. In a typical Arabic text, the frequencies of Arabic letters widely vary.…”

Section: Overview Of Arabic Lettersmentioning

confidence: 99%

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: Overview Of Arabic Lettersmentioning

confidence: 99%

Analysis of Handwritten Arabic Letters Using Selected Feature Extraction Techniques

Abandah¹,

Khedher²

2009

Int. J. Comp. Proc. Lang.

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“…This final result improved in relation to that gotten by the algorithm basic stage of 94.3% returns to the integration of an assessment function of the detected points regroupings based on topologic parameters. The survey of the tests statistics shows also that the effect on the baseline detection rate of the pseudo words number or the length in pixels of the treated text is less influencing to the output of the assessment function (the 2 nd stage of the algorithm) that to the output of the basic stage (the 1 st stage) as to been noted in [3].…”

Section: Resultsmentioning

confidence: 79%

“…This method has the defect to be very sensitive to the skew [3]. -The Hough transform method: The transposition in the polar coordinates space of the tracing points makes it possible to detect agglomerations of point images intersection defining the angle of writing lines skew [8] [9].…”

Section: The State Of the Artmentioning

confidence: 99%

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New Algorithm of Straight or Curved Baseline Detection for Short Arabic Handwritten Writing

Boubaker

Kherallah

Alimi

2009

2009 10th International Conference on Document Analysis and Recognition

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“…The baseline, on which the trajectories of aligned and joined characters are located, constitutes a useful segmentation marker for the acquired cursive script [2][3][4]. To detect it we decomposed the script path in groups of nearly aligned points according to the direction of their tangents to the trajectory.…”

Section: Introductionmentioning

confidence: 99%

Graphemes Segmentation for Arabic Online Handwriting Modeling

Boubaker¹,

Abed²,

Kherallah³

et al. 2014

Journal of Information Processing Systems

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Abstract-In the cursive handwriting recognition process, script trajectory segmentation and modeling represent an important task for large or open lexicon context that becomes more complicated in multi-writer applications. In this paper, we will present a developed system of Arabic online handwriting modeling based on graphemes segmentation and the extraction of its geometric features. The main contribution consists of adapting the Fourier descriptors to model the open trajectory of the segmented graphemes. To segment the trajectory of the handwriting, the system proceeds by first detecting its baseline by checking combined geometric and logic conditions. Then, the detected baseline is used as a topologic reference for the extraction of particular points that delimit the graphemes' trajectories. Each segmented grapheme is then represented by a set of relevant geometric features that include the vector of the Fourier descriptors for trajectory shape modeling, normalized metric parameters that model the grapheme dimensions, its position in respect to the baseline, and codes for the description of its associated diacritics.

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Baseline estimation for Arabic handwritten words

Cited by 77 publications

References 12 publications

Analysis of Handwritten Arabic Letters Using Selected Feature Extraction Techniques

Analysis of Handwritten Arabic Letters Using Selected Feature Extraction Techniques

New Algorithm of Straight or Curved Baseline Detection for Short Arabic Handwritten Writing

Graphemes Segmentation for Arabic Online Handwriting Modeling

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