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Cluster Computing

Erschienen in:

25.05.2017

Urdu ligature recognition using multi-level agglomerative hierarchical clustering

verfasst von: Naila Habib Khan, Awais Adnan, Sadia Basar

Erschienen in: Cluster Computing | Ausgabe 1/2018

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Abstract

Optical character recognition (OCR) system holds great significance in human-machine interaction. OCR has been the subject of intensive research especially for Latin, Chinese and Japanese script. Comparatively, little work has been done for Urdu OCR, due to the complexities and segmentation errors associated with its cursive script. This paper proposes an Urdu OCR system which aims at ligature-level recognition of Urdu text. This ligature based recognition approach overcomes the character-levelsegmentation problems associated with cursive scripts. A newly developed OCR algorithm is introduced that uses a semi-supervised multi-level clustering for categorization of the ligatures. Classification is performed using four machine learning techniques i.e. decision trees, linear discriminant analysis, naive Bayes and k-nearest neighbor (K-NN). The system was implemented and the results show 62, 61, 73 and 90% accuracy for decision tree, linear discriminant analysis, naive Bayes and K-NN respectively.

Vorheriger Artikel Region and texture based effective image extraction

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Titel: Urdu ligature recognition using multi-level agglomerative hierarchical clustering
verfasst von: Naila Habib Khan
Awais Adnan
Sadia Basar
Publikationsdatum: 25.05.2017
Verlag: Springer US
Erschienen in: Cluster Computing / Ausgabe 1/2018
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-017-0916-2

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