Handwritten Nastaleeq Script Recognition with BLSTM-CTC and ANFIS method

A recurrent neural network (RNN) has been successfully applied for recognition of cursive handwritten documents, both in English and Arabic scripts. Ability of RNNs to model context in sequence data like speech and text makes them a suitable candidate to develop OCR systems for printed Nastaleeq scripts (including Nastaleeq for which no OCR system is available to date). In this work, we have presented the results of applying RNN to printed Urdu text in Nastaleeq script. Bidirectional Long Short Term Memory (BLSTM) architecture with Connectionist Temporal Classification (CTC) output layer was employed to recognize printed Urdu text. The propose method use multidimensional BLSTM and ANFIS Method for OCR recognition. The ANFIS approach learns the rules and membership functions from data. ANFIS is an adaptive network. An adaptive network is network of nodes and directional links. These networks are learning a relationship between inputs and outputs. The Recognition error rate is 5.4 %. These results were obtained on synthetically generated UPTI dataset containing artificially degraded images to reflect some real-world scanning artifacts along with clean images. Comparison with shapematching based method is also presented.

[1]  Volker Märgner,et al.  Arabic Handwriting Recognition Competition , 2005, ICDAR.

[2]  Volkmar Frinken,et al.  Mode Detection in Online Handwritten Documents Using BLSTM Neural Networks , 2012, 2012 International Conference on Frontiers in Handwriting Recognition.

[3]  Volkmar Frinken,et al.  A Novel Word Spotting Method Based on Recurrent Neural Networks , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  G. S. Sable,et al.  Optical character recognition for printed text in Devanagari using ANFIS , 2013 .

[5]  Henry S. Baird,et al.  Document image defect models , 1995 .

[6]  Parisa A. Bahri,et al.  Hybrid intelligent scenario generator for business strategic planning by using ANFIS , 2009, Expert Syst. Appl..

[7]  Alex Graves,et al.  Supervised Sequence Labelling with Recurrent Neural Networks , 2012, Studies in Computational Intelligence.

[8]  Masaaki Nagata.  Japanese OCR Error Correction using Character Shape Similarity and Statistical Language Model , 1998, COLING-ACL.

[9]  Faisal Shafait,et al.  A segmentation-free approach to Arabic and Urdu OCR , 2013, Electronic Imaging.

[10]  Saad Bin Ahmed,et al.  Offline Printed Urdu Nastaleeq Script Recognition with Bidirectional LSTM Networks , 2013, 2013 12th International Conference on Document Analysis and Recognition.

[11]  C. V. Jawahar,et al.  Recognition of printed Devanagari text using BLSTM Neural Network , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[12]  Francesco Camastra,et al.  A SVM-based cursive character recognizer , 2007, Pattern Recognit..

[13]  C. V. Jawahar,et al.  BLSTM Neural Network Based Word Retrieval for Hindi Documents , 2011, 2011 International Conference on Document Analysis and Recognition.