Offline cursive Urdu-Nastaliq script recognition using multidimensional recurrent neural networks

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• 作者：Saeeda Naz^a ; ^eAuthor Vitae ; Arif I. Umar^aAuthor Vitae ; Riaz Ahmad^b ; ^fAuthor Vitae ; Saad B. Ahmed^cAuthor Vitae ; Syed H. Shirazi^aAuthor Vitae ; Imran Siddiqi^dAuthor Vitae ; Muhammad I. Razzak^c ; Author Vitae
• 关键词：Urdu OCR ; RNN ; MD-LSTM ; CTC ; UPTI database
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：12 February 2016
• 年：2016
• 卷：177
• 期：Complete
• 页码：228-241
• 全文大小：3152 K

文摘

Optical Character Recognition of cursive scripts remains a challenging task due to a large number of character shapes, inter- and intra-word overlaps, context sensitivity and diagonality of text. This paper presents an implicit segmentation based recognition system for Urdu text lines in Nastaliq script. The proposed technique relies on sliding overlapped windows on lines of text and extracting a set of statistical features. The extracted features are fed to a multi-dimensional long short term memory recurrent neural network (MDLSTM RNN) with a connectionist temporal classification (CTC) output layer that labels the character sequences. Experimental study of the proposed technique is carried out on the standard Urdu Printed Text-line Images (UPTI) database which comprises 10,000 text lines in Nastaliq font. Evaluations under different experimental settings realize promising recognition rates with a highest character recognition rate of 96.40%.