The optical character recognition of Urdu-like cursive scripts
Identifieur interne : 000119 ( Main/Exploration ); précédent : 000118; suivant : 000120The optical character recognition of Urdu-like cursive scripts
Auteurs : Saeeda Naz [Pakistan] ; Khizar Hayat [Pakistan, Oman] ; MUHAMMAD IMRAN RAZZAK [Arabie saoudite] ; MUHAMMAD WAQAS ANWAR [Pakistan] ; Sajjad A. Madani [Pakistan] ; Samee U. Khan [États-Unis]Source :
- Pattern recognition [ 0031-3203 ] ; 2014.
Descripteurs français
- Pascal (Inist)
English descriptors
- KwdEn :
Abstract
We survey the optical character recognition (OCR) literature with reference to the Urdu-like cursive scripts. In particular, the Urdu, Pushto, and Sindhi languages are discussed, with the emphasis being on the Nasta'liq and Naskh scripts. Before detaining the OCR works, the peculiarities of the Urdu-like scripts are outlined, which are followed by the presentation of the available text image databases. For the sake of clarity, the various attempts are grouped into three parts, namely: (a) printed, (b) handwritten, and (c) online character recognition. Within each part, the works are analyzed par rapport a typical OCR pipeline with an emphasis on the preprocessing, segmentation, feature extraction, classification, and recognition.
Affiliations:
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Le document en format XML
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<front><div type="abstract" xml:lang="en">We survey the optical character recognition (OCR) literature with reference to the Urdu-like cursive scripts. In particular, the Urdu, Pushto, and Sindhi languages are discussed, with the emphasis being on the Nasta'liq and Naskh scripts. Before detaining the OCR works, the peculiarities of the Urdu-like scripts are outlined, which are followed by the presentation of the available text image databases. For the sake of clarity, the various attempts are grouped into three parts, namely: (a) printed, (b) handwritten, and (c) online character recognition. Within each part, the works are analyzed par rapport a typical OCR pipeline with an emphasis on the preprocessing, segmentation, feature extraction, classification, and recognition.</div>
</front>
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