Statistical Features Extraction for Character Recognition using Recurrent Neural Network.

Saeeda Naz; ArifI Umar; SaadB Ahmed; Riaz Ahmad; SyedH Shirazi; Muhammad I Razzak; Amir Zaman

Author(s)

Saeeda Naz Department of Information Technology, Hazara University, Higher Education Department, KPK, Pakistan;GGPGC, No. 1, Abbottabad, Higher Education Department, KPK, Pakistan

ArifI Umar Department of Information Technology, Hazara University, Mansehra, Pakistan

SaadB Ahmed University of Technology, Kaiserslautern, Germany

Riaz Ahmad King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia

SyedH Shirazi Department of Information Technology, Hazara University, Mansehra, Pakistan

Muhammad I Razzak University of Technology, Kaiserslautern, Germany

Amir Zaman Department of Education, Abdul Wali Khan University Mardan, Pakistan

Abstract

Recent studies show that recurrent neural network provided promising results for character recognition. We have extracted number of features using sliding window approach from normalized Urdu Nasta'liq text line image. The text line is scanned from right to left and top to bottom by considering Urdu script properties and extracted geometrical or statistical features, zoning and raw pixels features. We conduct four studies like sliding window with non-overlapped frame, sliding window with overlapped area with previous frame, multiple zones in a frame and raw pixels. In this paper, we evaluated MDSLTM with CTC output layer on UPTI dataset for Urdu character recognition.

Publication Details

Page(s) 47-53

DOI DOI not available

Published Journal: Pakistan Journal of Statistics, Volume: 34, Issue: 1, Year: 2018

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References

Naz , S.,Hayat , K.,Razzak , M.I.,Anwar , M.W.,Madani , S.A.,Khan , S.U., 2014.The optical character recognition of Urdu-like cursive scripts,Pattern Recognition 47 1229 -1248

Ahmed , S.B.,Naz , S.,Razzak , M.I.,Rashid , S.F.,Afzal , M.Z.,Breuel , T.M., 2016.Evaluation of cursive and non-cursive scripts using recurrent neural networks,Neural Computing and Applications 27 603 -613

Naz , S.,Umar , A.I.,Ahmad , R.,Ahmed , S.B.,Shirazi , S.H.,Razzak , M.I., 2017.Urdu Nasta'liq text recognition system based on multi-dimensional recurrent neural network and statistical features,Neural Computing and Application 28 219 -23

Nishide , S.,Okuno , H.G.,Ogata , T.,Tani , J., 2011.Handwriting prediction based character recognition using recurrent neural network,In 2011 IEEE International Conference on Systems, Man, and Cybernetics (SMC) 2549 -2554

Graves , A.,Liwicki , M.,Fernández , S.,Bertolami , R.,Bunke , H.,Schmidhuber , J., 2009.A novel connectionist system for unconstrained handwriting recognition,IEEE transactions on pattern analysis and machine intelligence 31 855 -868

Graves , A., 2012.Offline arabic handwriting recognition with multidimensional recurrent neural networks. In Guide to OCR for Arabic scripts, 297 -313

Graves , A.,Schmidhuber , J., 2009.Offline handwriting recognition with multidimensional recurrent neural networks,In Advances in Neural Information Processing Systems 545 552 -

Liwicki , M.,Graves , A.,Bunke , H.,Schmidhuber , J., 2007.A novel approach to on-line handwriting recognition based on bidirectional long short-term memory networks,In Proc. 9th Int. Conf. on Document Analysis and Recognition 1 367 -371

Graves , A.,Mohamed , A.R.,G., 2013.Speech recognition with deep recurrent neural networks,In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 6645 -6649

Ul-Hasan , A.,Ahmed , S.B.,Rashid , F.,Shafait , F.,Breuel , T.M., 2013.Offline printed Urdu Nastaleeq script recognition with bidirectional LSTM networks,In 12th International Conference on Document Analysis and Recognition (ICDAR) 1061 -1065

Ahmed , S.B.,Naz , S.,Swati , S.,Razzak , M.I.,Khan , A.A.,Umar , A.I., 2014.UCOM offline dataset: A Urdu handwritten dataset generation,International Arab Journal of Information Technology 202 -

Khorsheed , M.S., 2007.Offline recognition of omni font Arabic text using the HMM ToolKit (HTK),Pattern Recognition Letters 28 1563 -1571

Bunke , H.,Bengio , S.,Vinciarelli , A., 2004.Offline recognition of unconstrained handwritten texts using HMMs and statistical language models,IEEE transactions on Pattern analysis and Machine intelligence 26 709 -720

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