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Zone and rule assisted recognition of Meitei-Mayek handwritten characters

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Abstract

This paper presents a two-stage approach for an improved recognition accuracy of handwritten Meitei Mayek characters. The first-stage is a CNN based recognition. The second-stage recognition is based on the zonal information and script-specific orthographic rules. The work is carried out on the finding that low recognition accuracy of Meitei Mayek handwritten characters is due to the presence of highly shape-similar confusing character pairs. First, the confusing character pairs are identified from the confusion matrix obtained while training and testing the CNN. For these confusing characters pairs, the zone information and certain script-specific orthographic rules are incorporated in the second recognition stage to distinguish between them. The second-stage recognition is carried out only on those characters which belong to one of the confusing character pairs. This makes the technique computationally more efficient than those where it is carried out for every output of the recognition stage. For zone identification, a novel method based on the row co-ordinates of characters present in the word is proposed. An improvement in the recognition accuracy is achieved with the two-stage approach compared to the single-stage recognition using the CNN. An improve ment in recognition accuracy of 3.36% is shown by our approach with a recognition accuracy of 91.86%.

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Data availability statement

The dataset used for training and validation purpose is available in the [Tezpur University] repository, [http://agnigarh.tezu.ernet.in/~sarat/resources.html]. The test dataset generated during and analysed during the current study are available from the corresponding author on reasonable request.

Notes

  1. https://en.wikipedia.org/wiki/Orthography

  2. https://tdil-dc.in/index.php?option=com_download&task=showresourceDetails&toolid=1874&lang=en.

References

  1. Ahmad I, Fink GA (2019) Handwritten arabic text recognition using multi-stage sub-core-shape hmms. Int J Doc Anal Recognit (IJDAR) 22(3):329–349

    Article  Google Scholar 

  2. Basu S, Das N, Sarkar R, Kundu M, Nasipuri M, Basu DK (2009) A hierarchical approach to recognition of hand-written bangla characters. Pattern Recogn 42(7):1467–1484

    Article  Google Scholar 

  3. Bhattacharya U, Chaudhuri BB (2008) Handwritten numeral databases of indian scripts and multistage recognition of mixed numerals. IEEE Trans Pattern Anal Mach Intell 31(3):444–457

    Article  Google Scholar 

  4. Bhattacharya U, Parui S, Shaw B (2007) A hybrid scheme for recognition of handwritten bangla basic characters based on hmm and mlp classifiers. In: Advances In Pattern Recognition, pp. 101–106. World Scientific

  5. Bhattacharya U, Shridhar M, Parui SK, Sen P, Chaudhuri B (2012) Offline recognition of handwritten bangla characters: an efficient two-stage approach. Pattern Anal Appl 15(4):445–458

    Article  MathSciNet  Google Scholar 

  6. Cheriet M, Kharma N, Liu CL, Suen C (2007) Character recognition systems: a guide for students and practitioners. John Wiley & Sons, Hoboken

    Book  Google Scholar 

  7. Das N, Acharya K, Sarkar R, Basu S, Kundu M, Nasipuri M (2012) A novel ga-svm based multistage approach for recognition of handwritten bangla compound characters. In: Proceedings of the International Conference on Information Systems Design and Intelligent Applications 2012 (INDIA 2012) held in Visakhapatnam, India, January 2012, pp. 145–152. Springer

  8. Das N, Sarkar R, Basu S, Saha PK, Kundu M, Nasipuri M (2015) Handwritten bangla character recognition using a soft computing paradigm embedded in two pass approach. Pattern Recogn 48(6):2054–2071

    Article  Google Scholar 

  9. Hijam D, Saharia S (2022) On developing complete character set Meitei Mayek handwritten characterdatabase. Vis Comput 38(2):525–539

    Article  Google Scholar 

  10. Hijam D, Saharia S, Nirmal Y (2018) Towards a complete character set Meitei Mayek handwritten character recognition. In: 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), pp. 1–5. IEEE

  11. Inkeaw P, Bootkrajang J, Marukatat S, Gonçalves T, Chaijaruwanich J (2019) Recognition of similar characters using gradient features of discriminative regions. Expert Syst Appl 134:120–137

    Article  Google Scholar 

  12. John J, Pramod K, Balakrishnan K, Chaudhuri BB (2014) A two stage approach for handwritten malayalam character recognition. In: 2014 14th International Conference on Frontiers in Handwriting Recognition, pp. 199–204. IEEE

  13. Kaufmann G, Bunke H (2000) Detection and correction of recognition errors in check reading. Int J Doc Anal Recogn 2(4):211–221

    Article  Google Scholar 

  14. Kayumov Z, Tumakov D, Mosin S (2020) Hierarchical convolutional neural network for handwritten digits recognition. Procedia Comput Sci 171:1927–1934

    Article  Google Scholar 

  15. Miller D, Boisen S, Schwartz R, Stone R, Weischedel R (2000) Named entity extraction from noisy input: speech and ocr. In: Sixth Applied Natural Language Processing Conference, pp. 316–324

  16. Phokharatkul P, Sankhuangaw K, Somkuarnpanit S, Phaiboon S, Kimpan C (2007) Off-line hand written thai character recognition using ant-miner algorithm. Int J Comput Inf Eng 1(8):2586–2591

    Google Scholar 

  17. Pitrelli JF, Perrone MP (2003) Confidence-scoring postprocessing for off-line handwritten-character recognition verification. In: Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings., pp. 278–282. IEEE

  18. Rahman AFR, Rahman R, Fairhurst MC (2002) Recognition of handwritten bengali characters: a novel multistage approach. Pattern Recogn 35(5):997–1006

    Article  Google Scholar 

  19. San Lwin C, Xiangqian W (2020) Myanmar handwritten character recognition from similar character groups using k-means and convolutional neural network. In: 2020 IEEE 3rd International Conference on Electronics and Communication Engineering (ICECE), pp. 172–176. IEEE

  20. Sarkhel R, Das N, Das A, Kundu M, Nasipuri M (2017) A multi-scale deep quad tree based feature extraction method for the recognition of isolated handwritten characters of popular indic scripts. Pattern Recogn 71:78–93

    Article  Google Scholar 

  21. Taghva K, Beckley R, Coombs J (2006) The effects of ocr error on the extraction of private information. In: International Workshop on Document Analysis Systems, pp. 348–357. Springer

  22. Tang YY, Tu LT, Liu J, Lee SW, Lin WW (1998) Offline recognition of chinese handwriting by multifeature and multilevel classification. IEEE Trans Pattern Anal Mach Intell 20(5):556–561

    Article  Google Scholar 

  23. Vamvakas G, Gatos B, Perantonis SJ (2010) Handwritten character recognition through two-stage foreground subsampling. Pattern Recogn 43(8):2807–2816

    Article  Google Scholar 

  24. Varghese KS, James A, Chandran S (2016) A novel tri-stage recognition scheme for handwritten malayalam character recognition. Procedia Technol 24:1333–1340

    Article  Google Scholar 

  25. Wakabayashi T, Pal U, Kimura F, Miyake Y (2009) F-ratio based weighted feature extraction for similar shape character recognition. In: 2009 10th International Conference on Document Analysis and Recognition, pp. 196–200. IEEE

  26. Wang Q, Lu Y (2017) Similar handwritten chinese character recognition using hierarchical cnn model. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 1, pp. 603–608. IEEE

  27. Wemhoener D, Yalniz IZ, Manmatha R (2013) Creating an improved version using noisy ocr from multiple editions. In: 2013 12th International Conference on Document Analysis and Recognition, pp. 160–164. IEEE

  28. Xiu Y, Wang Q, Zhan H, Lan M, Lu Y (2019) A handwritten chinese text recognizer applying multi-level multimodal fusion network. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 1464–1469. IEEE

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Authors and Affiliations

  1. Department of CSE, Gandhi Institute of Technology and Management (Deemed to Be University), Visakhapatnam, Andhra Pradesh, India

    Deena Hijam

  2. Department of CSE, Tezpur University, Tezpur, Assam, India

    Sarat Saharia

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  1. Deena Hijam
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  2. Sarat Saharia
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D.H. conceptualized the work and acquired the data, wrote the main manuscript text. S.S. reviewed and revised the manuscript.

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Correspondence to Deena Hijam.

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Hijam, D., Saharia, S. Zone and rule assisted recognition of Meitei-Mayek handwritten characters. Evol. Intel. 17, 2963–2980 (2024). https://doi.org/10.1007/s12065-024-00920-z

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