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Understanding NFC-Net: a deep learning approach to word-level handwritten Indic script recognition

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Abstract

This paper presents a deep learning architecture modified for resource-constrained environments, called Non-Fully-Connected Network or NFC-Net, based on convolutional neural network architecture in order to solve the problem of Indic script recognition from handwritten word images. NFC-Net mainly targets resource constraint environment where there is a limited computation power or inadequate training samples or restricted training time. Previous approaches to handwritten script recognition included handcrafted features such as structure-based features and texture-based features. In contrast, here our model learns relatively different features from raw input pixels using NFC-Net. Various parameters of the NFC-Net are adjusted to present a vast and comprehensive study of the neural net in the domain of handwritten script recognition. In order to evaluate the performance of the NFC-Net with suitable parameter estimation, a dataset of 18,000 handwritten multiscript word images consisting of 1500 text words from each of the 12 officially recognized Indic scripts has been considered and a maximum script recognition accuracy of 96.30% is noted. Our proposed model also performs better than some of the recently published script recognition methods in bi-script, tri-script, tetra-script and 12-script scenarios. It has been additionally tested on the RaFD and BHCCD datasets with improved results to prove dataset independency of our model.

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References

  1. Singh PK, Sarkar R, Nasipuri M (2015) Offline script identification from multilingual Indic-script documents: a state-of-the-art. Comput Sci Rev 15(C):1–28

    Article  MathSciNet  Google Scholar 

  2. Sangame SK, Ramteke RJ, Andure S, Gundge Y (2012) V: script identification of text words from a bilingual document using voting techniques. World J Sci Technol 2:114–119

    Article  Google Scholar 

  3. Roy K, Pal U (2006) Word-wise handwritten script separation for Indian postal automation. In: Proceedings of the international workshop on frontiers in handwriting recognition, La Baule, pp 521–526

  4. Roy K, Pal U, Chaudhuri BB (2005) Neural network based word-wise handwritten script identification system for Indian postal automation. In: Proceedings of the international conference on intelligent sensing and information processing, Chennai, pp 581–586. https://doi.org/10.1109/icisip.2005.1529455

  5. Sarkar R, Das N, Basu S, Kundu M, Nasipuri M, Basu DK (2010) Word level script identification from Bangla and Devanagari handwritten texts mixed with Roman scripts. J Comput 2(2):103–108

    Google Scholar 

  6. Memon MH, Li JP, Memon I, Arain QA, Memon MH (2017) Region based localized matching image retrieval system using color-size features for image retrieval. In: 2017 14th International computer conference on wavelet active media technology and information processing (ICCWAMTIP). https://doi.org/10.1109/iccwamtip.2017.8301481

  7. Memon MH, Memon I, Li JP, Arain QA (2018) IMRBS: image matching for location determination through a region-based similarity technique for CBIR. Int J Comput Appl. https://doi.org/10.1080/1206212x.2018.1468643

    Article  Google Scholar 

  8. Memon MH, Li JP, Memon I, Arain QA (2017) GEO matching regions: multiple regions of interests using content based image retrieval based on relative locations. Multimed Tools Appl 76:15377–15411

    Article  Google Scholar 

  9. Shaikh RA, Memon I, Hussain R, Maitlo A, Shaikh H (2018) A contemporary approach for object recognition based on spatial layout and low level features’ integration. Multimed Tools Appl. https://doi.org/10.1007/s11042-018-6796-5

    Article  Google Scholar 

  10. Shaikh RA, Li JP, Khan A, Memon I (2015) Biomedical image processing and analysis using Markov random fields. In: 2015 12th International computer conference on wavelet active media technology and information processing (ICCWAMTIP). https://doi.org/10.1109/iccwamtip.2015.7493970

  11. Singh PK, Sarkar R, Das N, Basu S, Nasipuri M (2014) Statistical comparison of classifiers for script identification from multi-script handwritten documents. Int J Appl Pattern Recognit 1(2):152–172

    Article  Google Scholar 

  12. Patil SB, Subbareddy NV (2002) Neural network-based system for script identification in Indian documents. Sadhana 27(1):83–97

    Article  Google Scholar 

  13. Khandelwal A, Choudhury P, Sarkar R, Basu S, Nasipuri M, Das N (2009) Text line segmentation for unconstrained handwritten document images using neighborhood connected component analysis. In: International conference on pattern recognition and machine intelligence, LNCS 5909. Springer, Berlin, pp 369–374

  14. Wahl FM, Wong KY, Casey RG (1982) Block segmentation and text extraction in mixed text/image documents. Comput Graph Image Process 20(4):375–390

    Article  Google Scholar 

  15. Hiremath PS, Shivashankar S (2008) Wavelet based co-occurrence histogram features for texture classification with an application to script identification in a document image. Pattern Recognit Lett 29(9):1182–1189. https://doi.org/10.1016/j.patrec.2008.01.012

    Article  Google Scholar 

  16. Ma H, Doermann D (2004) Word level script identification on scanned document images. In: Proceedings of the SPIE conference on document recognition and retrieval, San Jose, CA, USA, pp 124–135

  17. Peake GS, Tan TN (1998) Script and language identification from document images. In: Proceedings of the Asian conference computer vision, LNCS, vol 1352, pp 97–104

  18. Padma MC, Vijaya PA (2010) Global approach for script identification using wavelet packet based features. Int J Signal Process Image Process Pattern Recognit 3:29–40

    Google Scholar 

  19. Singh PK, Mondal A, Bhowmik S, Sarkar R, Nasipuri M (2014) Word-level script identification from multi-script handwritten documents. In: Proceedings of the 3rd international conference on frontiers in intelligent computing theory and applications (FICTA), pp 551–558

  20. Tan TN (1998) Rotation invariant texture features and their use in automatic script identification. IEEE Trans Pattern Anal Mach Intell 20(7):751–756. https://doi.org/10.1109/34.689305

    Article  Google Scholar 

  21. Hangarge M, Santosh KC, Pardeshi R (2013) Directional discrete Cosine transform for handwritten script identification. In: Proceedings of 12th IEEE international conference on document analysis and recognition (ICDAR), 2013, pp 344–348

  22. Pardeshi R, Chaudhuri BB, Hangarge M, Santosh KC (2014) Automatic handwritten Indian scripts identification. In: Proceedings of 14th IEEE international conference on frontiers in handwriting recognition (ICFHR), 2014, pp 375–380

  23. Chanda S, Pal S, Pal U (2008) Word-wise Sinhala, Tamil and English script identification using Gaussian kernel SVM. In: Proceedings of 19th IEEE international conference on pattern recognition, pp 1–4

  24. Chanda S, Pal S, Franke K, Pal U (2009) Two-stage approach for word-wise script identification. In: Proceedings of 10th IEEE International Conference on Document Analysis and Recognition (ICDAR), pp 926–930

  25. Swamy Das M, Sandhya Rani D, Reddy CRK (2012) Heuristic based script identification from multilingual text documents. In: Proceedings of 1st conference on recent advances in information technology (RAIT), pp 487–492

  26. Swamy Das M, Sandhya Rani D, Reddy CRK, Govadhan A (2011) Script identification from multilingual Telugu, Hindi and English text documents. Int J Wisdom Based Comput 1(3):79–85

    Google Scholar 

  27. Singh PK, Sarkar R, Nasipuri M, Doermann D (2015) Word-level script identification for handwritten Indic scripts. In: Proceedings of 13th IEEE international conference on document analysis and recognition (ICDAR), pp 1106–1110

  28. Obaidullah SM, Santosh KC, Halder C, Das N, Roy K (2017) Automatic Indic script identification from handwritten documents: page, block, line and word-level approach. Int J Mach Learn Cybern 10(1):87–106

    Article  Google Scholar 

  29. Singh PK, Sarkar R, Das N, Basu S, Kundu M, Nasipuri M (2018) Benchmark databases of handwritten Bangla-Roman and Devanagari-Roman mixed-script document images. Multimed Tools Appl 77(7):8441–8473

    Article  Google Scholar 

  30. Obaidullah SM, Goswami C, Santosh KC, Das N, Halder C, Roy K (2017) Separating Indic scripts with matra for effective handwritten script identification in multi-script documents. Int J Pattern Recognit Artif Intell 31(05):1753003

    Article  Google Scholar 

  31. Bhunia AK, Mukherjee S, Sain A, Bhattacharyya A, Bhunia AK, Roy PP, Pal U (2018) Indic handwritten script identification using offline-online multimodal deep network. arXiv preprint arXiv:1802.08568

  32. Ukil S, Ghosh S, Obaidullah SM, Santosh KC, Roy K, Das N (2018) Deep learning for word-level handwritten Indic script identification. arXiv preprint arXiv:1801.01627

  33. Pati PB, Ramakrishnan AG (2006) HVS inspired system for script identification in Indian multi-script documents. In: Lecture notes in computer science: international workshop document analysis systems, vol 3872, Nelson, 2006, pp 380–389

  34. Roy K, Majumder K (2008) Trilingual script separation of handwritten postal document. In: Proceedings of 6th Indian conference on computer vision, graphics & image processing, pp 693–700. https://doi.org/10.1109/icvgip.2008.29

  35. LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324

    Article  Google Scholar 

  36. Akhand MAH, Rahman MM, Shill PC, Islam S, Hafizur Rahman MM (2015) Bangla handwritten numeral recognition using convolutional neural network. In: 2015 IEEE international conference on electrical engineering and information communication technology (ICEEICT), 1–5

  37. Zhao H, Hu Y, Zhang J (2017) Character recognition via a compact convolutional neural network. In: 2017 International conference on digital image computing: techniques and applications (DICTA), 1–6

  38. Yuan A, Bai G, Yang P, Guo Y, Zhao X (2012) Handwritten English word recognition based on convolutional neural networks. In: 2012 international conference on frontiers in handwriting recognition, Bari, pp 207–212. https://doi.org/10.1109/ICFHR.2012.210

  39. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436

    Article  Google Scholar 

  40. Zhang Z (2016) Derivation of backpropagation in convolutional neural network. https://pdfs.semanticscholar.org/5d79/11c93ddcb34cac088d99bd0cae9124e5dcd1.pdf. Accessed 25 May 2018

  41. Langner O, Dotsch R, Bijlstra G, Wigboldus DH, Hawk ST, Van Knippenberg AD (2010) Presentation and validation of the Radboud faces database. Cogn Emot 24(8):1377–1388

    Article  Google Scholar 

  42. https://code.google.com/archive/p/cmaterdb/. Accessed on 02 Feb 2019

  43. Das N, Acharya K, Sarkar R, Basu S, Kundu M, Nasipuri M (2014) A benchmark image database of isolated Bangla handwritten compound characters. Int J Doc Anal Recognit (IJDAR) 17(4):413–431

    Article  Google Scholar 

  44. Roy S, Das N, Kundu M, Nasipuri M (2017) Handwritten isolated Bangla compound character recognition: a new benchmark using a novel deep learning approach. Pattern Recogn Lett 90:15–21

    Article  Google Scholar 

  45. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Rabinovich A (2015) Going deeper with convolutions. In: CVPR

  46. Krizhevsky A, Sutskever I, Hinton GE (2012). Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  47. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  48. Obaidullah SM, Karim R, Shaikh S, Halder C, Das N, Roy K (2015) Transform based approach for Indic script identification from handwritten document images. In: 2015 3rd International conference on signal processing, communication and networking (ICSCN). IEEE, pp 1–7

  49. Singh PK, Das S, Sarkar R, Nasipuri M (2016) Line parameter based word-level Indic script identification system. Int J Comput Vis Image Process (IJCVIP) 6(2):18–41

    Article  Google Scholar 

  50. Mukhopadhyay A, Singh PK, Sarkar R, Nasipuri M (2018) Handwritten Indic script recognition based on the Dempster–Shafer theory of evidence. J Intell Syst. https://doi.org/10.1515/jisys-2017-0431

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Jadavpur University, 188, Raja S.C. Mullick Road, Kolkata, West Bengal, 700032, India

    Soumyadeep Kundu, Sayantan Paul, Pawan Kumar Singh, Ram Sarkar & Mita Nasipuri

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  1. Soumyadeep Kundu
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  2. Sayantan Paul
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  3. Pawan Kumar Singh
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Correspondence to Pawan Kumar Singh.

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Kundu, S., Paul, S., Singh, P.K. et al. Understanding NFC-Net: a deep learning approach to word-level handwritten Indic script recognition. Neural Comput & Applic 32, 7879–7895 (2020). https://doi.org/10.1007/s00521-019-04235-4

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