Skip to main content
SpringerLink
Log in

Handwritten Devanagari Character Recognition Using Modified Lenet and Alexnet Convolution Neural Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Despite many advances, Handwritten Devanagari Character Recognition (HDCR) remains unsolved due to the presence of complex characters. For HDCR, the traditional feature extraction and classification techniques are limited to the datasets developed in the respective laboratory that are not available publicly. A standard benchmarking dataset is not available for HDCR that helps to develop deep learning models. To progress the performance of HDCR, in this study, we produced a dataset of 38,750 images of Devanagari numerals, and vowels are generated and made publicly available for fellow researchers in this domain. This data is collected from more than 3000 subjects of different age groups. Each character is extracted by a segmentation technique proposed here, which is limited to this application. Experiments are conducted on the dataset; three different Convolution Neural Networks (CNN) architecture is developed. 1. CNN, 2. Modified Lenet CNN (MLCNN) and 3. Alexnet CNN (ACNN). A Modified LCNN is proposed by changing the architecture of Lenet 5 CNN. Regular Lenet 5 has \(\mathrm{tanh}(x)\) as its activation function. Since the Devangari characters are nonlinear, non-linearity is introduced in the Networks by using Rectified Linear Unit. This solves the problem of vanishing gradient problem by \(\mathrm{tanh}(x)\). We achieved a recognition rate of 96% on training data and 94% on unseen data using CNN. MLCNN obtained an accuracy rate of 99% and 94% with less computational cost. Whereas, ACNN attained a recognition rate of 99% and 98% on unseen data. A series of experiments were conducted on the data with different combination splits of data and found a minimum loss of 0.001%. Such developments fill a significant percentage of the huge gap between real-world requirements and the actual performance of Devanagari recognizers.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Availability of data and material

The datasets generated during and/or analysed during the current study are available in the Mendeley repository, https://data.mendeley.com/datasets/pxrnvp4yy8/1

Code availability

The code that supports the findings of this study are available on request from the corresponding author. The code is not publicly available due to containing information that could compromise the privacy of research participants.

References

  1. Sahare, P., & Dhok, S. B. (2019). Robust character segmentation and recognition schemes for multilingual indian document Images. IETE Technical Review, 36(2), 209–222.

    Article  Google Scholar 

  2. Parekh, K. A., Goswami, M. M., & Mitra, S. K. (2020). Handwritten numeral recognition using polar histogram of low-level stroke features. In Proceedings of 3rd international conference on computer vision and image processing (pp. 169–181). Springer.

  3. Bansal, V., & Sinha, R. M. K. (2002). Segmentation of touching and fused Devanagari characters. Pattern recognition, 35(4), 875–893.

    Article  Google Scholar 

  4. Li, Z., Teng, N., Jin, M., & Lu, H. (2018). Building efficient CNN architecture for offline handwritten Chinese character recognition. International Journal on Document Analysis and Recognition (IJDAR), 21(4), 233–240.

    Article  Google Scholar 

  5. Kaur, S., & Sagar, B. B. (2019). Brahmi character recognition based on SVM (support vector machine) classifier using image gradient features. Journal of Discrete Mathematical Sciences and Cryptography, 22(8), 1365–1381.

    Article  Google Scholar 

  6. Setlur, S. (2009). Guide to OCR for indic scripts. In V. Govindaraju (Ed.). Springer.

  7. Bathla, A. K., Gupta, S. K., & Jindal, M. K. (2016, March). Challenges in recognition of Devanagari Scripts due to segmentation of handwritten text. In 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom) (pp. 2711–2715). IEEE.

  8. https://data.mendeley.com/datasets/pxrnvp4yy8/1

  9. Bharath, A., & Madhvanath, S. (2011). HMM-based lexicon-driven and lexicon-free word recognition for online handwritten Indic scripts. IEEE transactions on pattern analysis and machine intelligence, 34(4), 670–682.

    Article  Google Scholar 

  10. Soora, N. R., & Deshpande, P. S. (2018). Review of feature extraction techniques for character recognition. IETE Journal of Research, 64(2), 280–295.

    Article  Google Scholar 

  11. Sharma, A. K., Adhyaru, D. M., & Zaveri, T. H. (2020). A survey on Devanagari character recognition. In Smart systems and IoT: Innovations in computing (pp. 429–437). Springer.

  12. Goswami, M. M., Prajapati, H. B., & Dabhi, V. K. (2011). Classification of printed Gujarati characters using SOM based k-Nearest Neighbor Classifier. In 2011 International conference on image information processing (pp. 1–5). IEEE.

  13. Sinha, R. M. K., & Mahabala, H. N. (1979). Machine recognition of Devanagari script. IEEE Transactions on Systems, Man and Cybernetics, 9(8), 435–441.

    Article  MathSciNet  Google Scholar 

  14. Jayanthi, K., Suzuki, A., Kanai, H., Kawazoe, Y., Kimura, M., & Kido, K. (1989, November). Devanagari character recognition using structure analysis. In Fourth IEEE Region 10 international conference TENCON (pp. 363–366). IEEE.

  15. Bansal, V., & Sinha, M. K. (2001, September). A complete OCR for printed Hindi text in Devanagari script. In Proceedings of Sixth International Conference on Document Analysis and Recognition (pp. 0800–0800). IEEE Computer Society.

  16. Sharma, N., Pal, U., Kimura, F., & Pal, S. (2006). Recognition of offline handwritten devnagari characters using quadratic classifier. In Computer vision, graphics and image processing (pp. 805–816). Springer.

  17. Arora, S., Bhattacharjee, D., Nasipuri, M., Basu, D. K., Kundu, M., & Malik, L. (2009, December). Study of different features on handwritten Devnagari character. In 2009 Second international conference on emerging trends in engineering & technology (pp. 929–933). IEEE.

  18. Kumar, S. (2009). Performance comparison of features on Devanagari hand-printed dataset. International Journal of Recent Trends in Engineering, 1(2), 33.

    Google Scholar 

  19. Arora, S., Bhattacharjee, D., Nasipuri, M., Basu, D. K., & Kundu, M. (2010). Recognition of non-compound handwritten devnagari characters using a combination of mlp and minimum edit distance. arXiv preprint arXiv:1006.5908.

  20. Pal, U., Chanda, S., Wakabayashi, T., & Kimura, F. (2008). Accuracy improvement of Devnagari character recognition combining SVM and MQDF. In Proceedings of 11th International Conferenece on Frontiers Handwrit. Recognition (pp. 367–372).

  21. Kale, K. V., Deshmukh, P. D., Chavan, S. V., Kazi, M. M., & Rode, Y. S. (2013). Zernike moment feature extraction for handwritten Devanagari compound character recognition. In 2013 Science and information conference (pp. 459–466). IEEE.

  22. Arora, S., Bhatcharjee, D., Nasipuri, M., & Malik, L. (2007). A two stage classification approach for handwritten Devnagari characters. In International conference on computational intelligence and multimedia applications (ICCIMA 2007) (Vol. 2, pp. 399–403). IEEE.

  23. Hanmandlu, M., Murthy, O. R., & Madasu, V. K. (2007, December). Fuzzy model based recognition of handwritten Hindi characters. In 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications (DICTA 2007) (pp. 454–461). IEEE.

  24. Aneja, N., & Aneja, S. (2019, July). Transfer learning using CNN for handwritten Devanagari character recognition. In 2019 1st International Conference on Advances in Information Technology (ICAIT) (pp. 293–296). IEEE.

  25. Arora, S., Bhattacharjee, D., Nasipuri, M., Malik, L., Kundu, M., & Basu, D. K. (2010). Performance comparison of SVM and ANN for handwritten devnagari character recognition. arXiv preprint arXiv:1006.5902

  26. Lu, S., Lu, Z., & Zhang, Y. D. (2019). Pathological brain detection based on AlexNet and transfer learning. Journal of computational science, 30, 41–47.

    Article  Google Scholar 

  27. Shima, Y., Nakashima, Y., & Yasuda, M. (2018, April). Handwritten Digits Recognition by Using CNN Alex-Net Pre-trained for Large-scale Object Image Dataset. In Proceedings of the 3rd international conference on multimedia systems and signal processing (pp. 36–40).

  28. Kumar, M., Jindal, M. K., Sharma, R. K., & Jindal, S. R. (2019). Character and numeral recognition for non-Indic and Indic scripts: A survey. Artificial Intelligence Review, 52(4), 2235–2261.

    Article  Google Scholar 

  29. Niu, X. X., & Suen, C. Y. (2012). A novel hybrid CNN–SVM classifier for recognizing handwritten digits. Pattern Recognition, 45(4), 1318–1325.

    Article  Google Scholar 

  30. Jiang, B., He, J., Yang, S., Fu, H., Li, T., Song, H., & He, D. (2019). Fusion of machine vision technology and AlexNet-CNNs deep learning network for the detection of postharvest apple pesticide residues. Artificial Intelligence in Agriculture, 1, 1–8.

    Article  Google Scholar 

  31. Chaudhuri, B. B., & Pal, U. (1997, August). An OCR system to read two Indian language scripts: Bangla and Devnagari (Hindi). In Proceedings of the fourth international conference on document analysis and recognition (Vol. 2, pp. 1011–1015). IEEE.

  32. Kompalli, S., Nayak, S., Setlur, S., & Govindaraju, V. (2005, August). Challenges in OCR of Devanagari documents. In Eighth International Conference on Document Analysis and Recognition (ICDAR'05) (pp. 327–331). IEEE.

  33. Pal, U., Sharma, N., Wakabayashi, T., & Kimura, F. (2007, September). Handwritten numeral recognition of six popular Indian scripts. In Ninth International Conference on Document Analysis and Recognition (ICDAR 2007) (Vol. 2, pp. 749–753). IEEE.

  34. Soora, N. R., & Deshpande, P. S. (2017). Novel geometrical shape feature extraction techniques for multilingual character recognition. IETE Technical Review, 34(6), 612–621.

    Article  Google Scholar 

  35. Prashanth, D. S., Mehta, R. V. K., & Sharma, N. (2020). Classification of Handwritten Devanagari number-an analysis of pattern recognition tool using neural network and CNN. Procedia Computer Science, 167, 2445–2457.

    Article  Google Scholar 

  36. Prashanth, D. S., & Panini, C. N. (2017). KNN classification of Kannada Characters using Hu’s Seven Variants and Zernike Moment. Nagendra Panini Challa on Aug, 10.

  37. Latif, G., et al. (2018). Deep convolutional neural network for recognition of unified multi-language handwritten numerals. 2018 IEEE 2nd International workshop on Arabic and derived script analysis and recognition (ASAR). IEEE.

  38. Chakraborty, B., et al. (2018). Does deeper network lead to better accuracy: a case study on handwritten Devanagari characters. 2018 13th IAPR International Workshop on Document Analysis Systems (DAS). IEEE.

  39. Rojatkar, D. V., Chinchkhede, K. D., & Sarate, G. G. (2013). Design and analysis of LRTB feature based classifier applied to handwritten Devnagari characters: A neural network approach. In 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE,.

  40. Ashokkumar, P., Siva Shankar, G., Gautam Srivastava, Praveen Kumar Reddy Maddikunta, & Thippa Reddy Gadekallu. 2021. “A Two-stage Text Feature Selection Algorithm for Improving Text Classification”. ACM Trans. Asian Low-Resour. Lang. Inf. Process. 20, 3, Article 49 (July 2021), 19 pages. DOI:https://doi.org/10.1145/3425781

  41. Dhanamjayulu, C., Nizhal, U. N., Kumar Reddy Maddikunta, P., Thippa Reddy Gadekallu, Celestine Iwendi, Chuliang Wei, & Qin Xin. (2021). Identification of malnutrition and prediction of BMI from facial images using real-time image processing and machine learning. IET IMAGE PROCESSING.

  42. Gadekallu, T. R., Alazab, M., Kaluri, R., Reddy Maddikunta, P. K., Bhattacharya, S., Lakshmanna, K., & Parimala, M. (2021). Hand gesture classification using a novel CNN-crow search algorithm. Complex & Intelligent Systems, 7, 1–14.

    Article  Google Scholar 

  43. Kothai, G., Poovammal, E., Dhiman, G., Ramana, K., Sharma, A., AlZain, M. A., & Masud, M. (2021). A new hybrid deep learning algorithm for prediction of wide traffic congestion in smart cities. Wireless Communications and Mobile Computing, 2021, 1.

    Article  Google Scholar 

Download references

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. SCSVMV University, Kanchipuram, India

    Duddela Sai Prashanth & R. Vasanth Kumar Mehta

  2. Sahyadri College of Engineering and Management, Mangaluru, India

    Duddela Sai Prashanth

  3. Department of Artificial Intelligence and Data Science, Annamacharya Institute of Technology and Sciences, Rajampet, Andhra Pradesh, India

    Kadiyala Ramana

  4. Department of Electrical and Computer Engineering, San Francisco State University, San Francisco, CA, 94132, USA

    Vidhyacharan Bhaskar

Authors
  1. Duddela Sai Prashanth
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. Vasanth Kumar Mehta
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kadiyala Ramana
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Vidhyacharan Bhaskar
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Duddela Sai Prashanth: Conceptualization, Methodology, Software, Visualization R Vasanth Kumar Mehta: Data curation, Writing—original draft, Data Analysis, Investigation. Kadiyala Ramana: Software, Validation, Editing. Vidya Charan Bhaskar: Supervision, Writing—review & editing.

Corresponding author

Correspondence to Vidhyacharan Bhaskar.

Ethics declarations

Conflict of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Prashanth, D.S., Mehta, R.V.K., Ramana, K. et al. Handwritten Devanagari Character Recognition Using Modified Lenet and Alexnet Convolution Neural Networks. Wireless Pers Commun 122, 349–378 (2022). https://doi.org/10.1007/s11277-021-08903-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-021-08903-4

Keywords

Search

Navigation