Urdu ligature recognition using multi-level agglomerative hierarchical clustering

• Published in: Cluster computing Vol. 21; no. 1; pp. 503 - 514
• Main Authors: Khan, Naila Habib, Adnan, Awais, Basar, Sadia
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2018; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Classification; Cluster analysis; Clustering; Computer Communication Networks; Computer Science; Decision analysis; Decision trees; Dictionaries; Discriminant analysis; Handwriting; Ideograph recognition; Language policy; Machine learning; Neural networks; Operating Systems; Optical character recognition; Processor Architectures; R&D; Research & development; Segmentation; Urdu language; Pakistan; Agglomerative; Clustering; Urdu; Classification; OCR
• ISSN: 1386-7857; 1573-7543
• DOI: 10.1007/s10586-017-0916-2

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.