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Zone-based keyword spotting in Bangla and Devanagari documents

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Abstract

In this paper, we present a word spotting system in text lines for offline Indic scripts such as Bangla (Bengali) and Devanagari. Recently, it was shown that the zone-wise recognition method improves word recognition performance than the conventional full word recognition system in Indic scripts, like Bangla, Devanagari, Gurumukhi (Roy et al. in Pattern Recogn 60: 1057-1075, 26; Bhunia et al. in Pattern Recogn 79: 12–31, 6). Inspired from this idea we consider the zone segmentation approach and use middle zone information to improve the traditional word spotting performance. To avoid the problem of zone segmentation using heuristic approach, we propose here a new HMM based approach to segment the upper and lower zone components from the text line images. The candidate keywords are searched from a line without segmenting characters or words. Also, we propose a feature combining foreground and background information of text line images for keyword-spotting by character filler models. A significant improvement in performance is noted by using both foreground and background information instead of the individual one. Pyramid Histogram of Oriented Gradient (PHOG) feature has been used in our word spotting framework. From the experiment, it has been noted that the proposed zone-segmentation based system outperforms traditional approaches of word spotting.

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References

  1. Ahmed R, Al-Khatib WG, Mahmoud S (2016) A survey on handwritten documents word spotting. Int J Multimed Inf Retr:1–17

  2. Almazán J, Gordo A, Fornés A, Valveny E (2014) Word spotting and recognition with embedded attributes. IEEE Trans Pattern Anal Mach Intell 36(12):2552–2566

    Article  Google Scholar 

  3. Antonacopoulos A, Downton A (2007) Special issue on the analysis of historical documents. Int J Doc Anal Recognit 9(2):75–77

    Article  Google Scholar 

  4. Bai Y, Guo L, Jin L, Huang Q (2009) A novel feature extraction method using PHOG for smile recognition. In: Proc International Conference on Image Processing, pp 3305–3308

  5. Bhunia AK, Das A, Roy PP, Pal U (2015) A comparative study of features for handwritten Bangla text recognition. In: International Conference on Document Analysis and Recognition, pp 636–640

  6. Bhunia AK, Roy PP, Mohta A, Pal U (2018) Cross-language framework for word recognition and spotting of Indic scripts. Pattern Recogn 79:12–31

    Article  Google Scholar 

  7. Bhunia AK, Das A, Bhunia AK, Kishore PSR, Roy PP (2019) Handwriting recognition in low-resource scripts using adversarial learning. In: IEEE Conference on Computer Vison and Pattern Recognition(CVPR), [Accepted]

  8. Chaudhuri BB, Pal U (1998) A complete printed Bangla OCR system. Pattern Recogn 31(5):531–549

    Article  Google Scholar 

  9. Das A, Bhunia AK, Roy PP, Pal U (2015). Handwritten word spotting in Indic scripts using foreground and background information. In: Proc. Asian Conference on Pattern Recognition (ACPR), pp 426–430

  10. Dutta K, Krishnan P, Mathew M, Jawahar CV (2018) Towards spotting and recognition of handwritten words in Indic Scripts. In: 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR). IEEE, pp 32–37

  11. Fischer A, Keller A, Frinken V, Bunke H (2012) Lexicon-free handwritten word spotting using character HMMs. Pattern Recogn Lett 33:934–942

    Article  Google Scholar 

  12. Frinken V, Fischer A, Manmatha R, Bunke H (2012) A novel word spotting method based on recurrent neural networks. IEEE Trans Pattern Anal Mach Intell 34(2):211–224

    Article  Google Scholar 

  13. Jayadevan R, Kolhe SR, Patil PM, Pal U (2012) Automatic processing of handwritten bank cheque images: a survey. Int J Doc Anal Recogn 15(4):267–296

    Article  Google Scholar 

  14. Kavallieratou E, Fakotakis N, Kokkinakis G (2001) Slant estimation algorithm for OCRsystem. Pattern Recogn 34:2515–2522

    Article  Google Scholar 

  15. Leydier Y, Ouji A, Le-Bourgeois F, Emptoz H (2009) Towards an omni-lingual word retrieval system for ancient manuscripts. Pattern Recogn 42:2089–2105

    Article  Google Scholar 

  16. Leydier Y, Ouji A, LeBourgeois F, Emptoz H (2009) Towards an omnilingual word retrieval system for ancient manuscripts. Pattern Recogn 42(9):2089–2105

    Article  Google Scholar 

  17. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  18. Nagy G, Lopresti D (2006) Interactive document processing and digital libraries. In: Proc. 2nd Internat. Workshop on Document Image Analysis for Libraries, pp 2–11

  19. Niyogi D, Srihari SN, Govindaraju V (1997) Analysis of printed forms. In: Bunke H, Wang PSP (eds) Handbook of character recognition and document image analysis. World Scientific Publishing, pp 485–502

  20. Rath TM, Manmatha R (2007) Word spotting for historical documents. IJDAR 139–152

  21. Rothacker L, Sudholt S, Rusakov E, Kasperidus M, Fink GA (2017) Word hypotheses for segmentation-free word spotting in historic document images. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 1. IEEE, pp 1174–1179

  22. Rothfeder JL, Feng S, Rath TM (2003) Using corner feature correspondences to rank word images by similarity. In: Proc Workshop on Document Image Analysis and Retrieval, pp 30–35

  23. Roy PP, Pal U, Lladós J (2008) Morphology based handwritten line segmentation using foreground and background information. In: Proc International Conference on Frontiers in Handwriting Recognition, pp 241–246

  24. Roy PP, Pal U, Lladós J (2012) Text line extraction in graphical documents using background and foreground information. Int J Doc Anal Recognit 15(3):227–241

    Article  Google Scholar 

  25. Roy PP, Rayar F, Ramel JY (2015) Word spotting in historical documents using primitive based dynamic programming. Image Vis Comput 44:15–28

    Article  Google Scholar 

  26. Roy PP, Bhunia AK, Das A, Dey P, Pal U (2016) HMM-based Indic handwritten word recognition using zone segmentation. Pattern Recogn 60:1057–1075

    Article  Google Scholar 

  27. Roy PP, Bhunia AK, Bhattacharyya A, Pal U (2018) Word searching in scene image and video frame in multi-script scenario using dynamic shape coding. Multimed Tools Appl:1–35

  28. Rusinol M et al (2011) Browsing heterogeneous document collections by a segmentation-free word spotting method. In: Proc. International Conference on Document Analysis and Recognition, pp 63–67

  29. Sakoe H, Chiba S (1978) Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans Acoust Speech Signal Process 26(1):43–49

    Article  Google Scholar 

  30. Serrano JR, Perronnin F (2009) Handwritten word-spotting using hidden Markov models and universal vocabularies. Pattern Recogn 42(9):2106–2116

    Article  Google Scholar 

  31. Srihari SN, Keubert EJ (1997) Integration of handwritten address interpretation technology into the United States postal service remote computer reader system. In: Proc International Conference on Document Analysis and Recognition, pp 892–896

  32. Srihari SN, Huang C, Srinivasan H (2005) A search engine for handwritten documents. Document Recognition and Retrieval, pp.66–75

  33. Sudholt S, Fink GA (2016) PHOCNet: a deep convolutional neural network for word spotting in handwritten documents. In: 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), October. IEEE, pp 277–282

  34. Sudholt S, Fink GA (2017) Evaluating word string embeddings and loss functions for CNN-based word spotting. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), Vol. 1, November. IEEE, pp 493–498

  35. Tarafdar A, Mondal R, Pal S, Pal U, Kimura F (2010) Shape code based word-image matching for retrieval of Indian multi-lingual documents. In: Proc International Conference on Pattern Recognition, pp 1989–1992

  36. Wshah S, Kumar G, Govindaraju V (2014) Statistical script independent word spotting in offline handwritten documents. Pattern Recogn 47(3):1039–1050

    Article  Google Scholar 

  37. S. Young. The HTK book, Version 3.4, 2006.

  38. Zhang X, Pal U, Tan CL (2014) Segmentation-free Keyword Spotting for Bangla Handwritten Documents. In: Proc. International Conference on Frontiers in Handwriting Recognition, pp 381–386

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Correspondence to Ayan Kumar Bhunia.

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Bhunia, A.K., Roy, P.P., Sain, A. et al. Zone-based keyword spotting in Bangla and Devanagari documents. Multimed Tools Appl 79, 27365–27389 (2020). https://doi.org/10.1007/s11042-019-08442-y

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