KHATT: An open Arabic offline handwritten text database

Mahmoud, Sabri A.; Ahmad, Irfan; Al-Khatib, Wasfi G.; Alshayeb, Mohammad; Parvez, Mohammad Tanvir; Märgner, Volker; Fink, Gernot A.

doi:10.1016/j.patcog.2013.08.009

Cited by 159 publications

(64 citation statements)

References 42 publications

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“…KHATT [146,147] is a comprehensive database of Arabic handwritten text comprising 1000 forms produced by same number of writers from different countries. Each form is scanned at three different resolutions, 200, 300 and 600 dpi.…”

Section: Khatt Databasementioning

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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Section: Khatt Databasementioning

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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“…Experiments were conducted on handwritten images (word/text line) drawn from four different datasets [10][11][12][13]. The sample consists of 350 images of Arabic/Persian text.…”

Section: Resultsmentioning

confidence: 99%

“…Similarly two columns (one to the left and one to the right) are padded to the image (The necessity of such operation will be highlighted later). The sample images, on which the experimentation is carried out, have been drawn from datasets which are already binarized and de-noised [10][11][12][13]. Hence, no further binarization or noise removal is applied.…”

Section: A Preprocessingmentioning

confidence: 99%

“…Database contains totally 26459 handwritten images. KHATT [12] dataset consists of 1000 handwritten forms written by 1000 distinct writers from different countries Overall, each writer has written 6 paragraphs: 1 fixed text paragraph (written twice), 2 randomly selected paragraphs, and 2 optional paragraphs about subject of his interest. The fourth dataset used is a Persian dataset which is part of a multi-lingual dataset introduced in [11].…”

Section: Datasetsmentioning

confidence: 99%

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Graph Modeling based Segmentation of Handwritten Arabic Text into Constituent Subwords

Ghaleb¹,

Nagabhushan²,

Pal³

2016

IJIGSP

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Abstract-Segmentation of Arabic text is a major challenge that shall be addressed by any recognition system. The cursive nature of Arabic writing makes it necessary to handle the segmentation issue at various levels. Arabic text line can be viewed as a sequence of words which in turn can be viewed as a sequence of subwords. Sub-words have the frequently encountered intrinsic property of sharing the same vertical space which makes vertical projection based segmentation technique inefficient. In this paper, the task of segmenting handwritten Arabic text at sub-word level is taken up. The proposed algorithm is based on pulling away the connected components to overcome the impossibility of separating them by vertical projection based approach. Graph theoretic modeling is proposed to solve the problem of connected component extraction. In the sequel, these components are subjected to thorough analysis in order to obtain the constituent sub-words where a sub-word may consist of many components. The proposed algorithm was tested using variety of handwritten Arabic samples taken from different databases and the results obtained are encouraging.

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“…Much progress of such systems has been triggered thanks to the availability of public datasets. Examples include the IFN/ENIT [37] and KHATT [38] datasets for offline handwriting recognition and writer identification; the APTI database [39] for printed word recognition; and the ADAB dataset [40] that works on online handwriting recognition.…”

Section: Literature Reviewmentioning

confidence: 99%

Open Datasets and Tools for Arabic Text Detection and Recognition in News Video Frames

et al. 2018

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Abstract:Recognizing texts in video is more complex than in other environments such as scanned documents. Video texts appear in various colors, unknown fonts and sizes, often affected by compression artifacts and low quality. In contrast to Latin texts, there are no publicly available datasets which cover all aspects of the Arabic Video OCR domain. This paper describes a new well-defined and annotated Arabic-Text-in-Video dataset called AcTiV 2.0. The dataset is dedicated especially to building and evaluating Arabic video text detection and recognition systems. AcTiV 2.0 contains 189 video clips serving as a raw material for creating 4063 key frames for the detection task and 10,415 cropped text images for the recognition task. AcTiV 2.0 is also distributed with its annotation and evaluation tools that are made open-source for standardization and validation purposes. This paper also reports on the evaluation of several systems tested under the proposed detection and recognition protocols.

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KHATT: An open Arabic offline handwritten text database

Cited by 159 publications

References 42 publications

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

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

Graph Modeling based Segmentation of Handwritten Arabic Text into Constituent Subwords

Open Datasets and Tools for Arabic Text Detection and Recognition in News Video Frames

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