Survey and bibliography of Arabic optical text recognition

Al-Badr, B.; Mahmoud, Sabri A.

doi:10.1016/0165-1684(94)00090-m

Cited by 195 publications

(100 citation statements)

References 37 publications

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“…[3] B. Cursive handwriting OCR systems Research on OCR systems for cursive handwritten Latin text is very intensive. The study of segmentation is still ongoing with varying degrees of success in character segmentation.…”

Section: A Arabic Ocr Systemsmentioning

confidence: 99%

A base-line character recognition for Syriac-Aramaic

Tse

Bigan

2007

2007 IEEE International Conference on Systems, Man and Cybernetics

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Abstract-Serto is the cursive alphabet of Syriac-Aramaic, which is used by the largest corpus of documents in libraries in Aramaic. A lingua franca, and often a source language, Aramaic has influenced major Judaic, Christian and Islamic thoughts as well as the development of science. The script is cursive, e.g. Arabic, and consequently it has a hand-writing appearance compared to Latin. Serto, and Aramaic in practice, has not an automatic character recognition system, OCR. Most library documents are reproductions using printed characters. The readers would strongly benefit from having an OCR, as these reproductions are predominantly books, printed in the pre-computer era. We propose a segmentation-free OCR using linear symmetry features with an individual threshold for the tensors of the characters, and an ordered search sequence. It yields ~ 90 % correctly identified characters in the average. As a first recognition scheme for Serto, it represents a base-line OCR for Syriac-Aramaic.

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Section: A Arabic Ocr Systemsmentioning

confidence: 99%

A base-line character recognition for Syriac-Aramaic

Tse

Bigan

2007

2007 IEEE International Conference on Systems, Man and Cybernetics

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“…It is also a minority Arabic language used in different countries including Afghanistan, Nigeria and Iran. Referring to 1974, the Arabic language was a dominant spoken language in addition to the six main languages listed by the United Nations as official languages side by side with English, Chinese, Spanish, Arabic, French, Russian [1]. With over 1.6 billion Muslims around the globe such as Indonesia, Malaysia, Pakistan, India and Tanzania, the Arabic language is a second language in liturgical and intellectual fields.…”

Section: Introductionmentioning

confidence: 99%

Efficient Approach to Segment Ligatures and Open Characters in offline Arabic Text

2017

IJCCIE

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Abstract-This paper researches offline Arabic handwriting recognition. It introduces a new approach to segmentation ligature and open Arabic character based on the structural perspective dealing with sub-words/words, including dots to recognize individual letters. Segmentation approaches that have been integrated into the recognition phase have the capability to deal with ligatures and closed characters issues. This complex problem is due to the cursive writing nature of the Arabic language. This paper also develops an Arabic character algorithm on segmented, pixel based and centre reign (CR) to recognize the letters. The evaluation results are stated in the IFN/ENIT and IAM database which indicate the recognition rate and the effectiveness of our system.

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“…Arabic is the official script of Arabic and Persian countries which places it in a fairly high position on the ranking of the world mostly used scripts. Still, the number of studies done on the field of Arabic writing recognition is relatively low [2][3]. Because the Arabic writing is based on an alphabet and rules different from those of Latin, it makes it a foreign writing for the majority of our scientific community.…”

Section: Introductionmentioning

confidence: 99%

Multi-level arabic handwritten words recognition

Miled

Cheriet

Olivier

1998

Advances in Pattern Recognition

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In this paper, we present a strategy of Arabic words recognition by combining two levels which are based on global and analytical approaches according to the topological properties of Arabic handwriting. In the first level (global), we consider the visual indices which can be generated by: diacritics and strokes (denoted tracing) that form the main shapes of the word. Each word is described as a sequence of visual indices which is treated by a "global" classifier based on Hidden Markov Model (HMM). In the second level, the word is segmented into graphemes, then each grapheme is transformed into a HMM observation by a vector quantization phase. An analytical HMM is developed in order to manage the observation sequences. At this level the diacritics are not taken in consideration which allows to reduce the number of estimated character models. Finally we combine the two approaches to decide on the class of an unknown word. In fact, the global model serves as a filter. It produces a set of hypotheses to the analytical model, which in turns, defines and outputs the final decision.

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Survey and bibliography of Arabic optical text recognition

Cited by 195 publications

References 37 publications

A base-line character recognition for Syriac-Aramaic

A base-line character recognition for Syriac-Aramaic

Efficient Approach to Segment Ligatures and Open Characters in offline Arabic Text

Multi-level arabic handwritten words recognition

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