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Identification of handwritten Gujarati alphanumeric script by integrating transfer learning and convolutional neural networks

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Abstract

Offline handwriting recognition is an important application of pattern recognition that has attracted a lot of interest from researchers. Transforming any handwritten material into machine-readable text data by extracting hidden patterns and comprehending the texts from the documents is a complex process. There are 22 scheduled languages in India and Gujarati is one among them. There are several optical character recognition issues (OCR) in Gujarati and it is difficult to identify universal invariant patterns and irregularities in handwritten Gujarati script. The lack of a big benchmark dataset is another important issue with handwritten Gujarati script. This issue was identified, and we built a dataset with 75600 images spanning 54 Gujarati character classes. Although, this dataset is reasonably large, it is still not large enough to learn deep neural networks from scratch due to overfitting concerns. To address this problem, we have integrated transfer learning with CNN for Gujarati handwritten character recognition. We have used 5 distinct pre-trained models and have achieved approximately 97% accuracy on images of 54 different classes.

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Acknowledgements

The authors of this paper are grateful to the Institute of Technology, Nirma University for their support and motivation during this research.

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Authors and Affiliations

  1. Institute of Technology, Nirma University, Ahmedabad, India

    Krishn Limbachiya, Ankit Sharma, Priyank Thakkar & Dipak Adhyaru

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  1. Krishn Limbachiya
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  2. Ankit Sharma
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  3. Priyank Thakkar
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  4. Dipak Adhyaru
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Correspondence to Ankit Sharma.

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Limbachiya, K., Sharma, A., Thakkar, P. et al. Identification of handwritten Gujarati alphanumeric script by integrating transfer learning and convolutional neural networks. Sādhanā 47, 102 (2022). https://doi.org/10.1007/s12046-022-01864-9

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  • DOI: https://doi.org/10.1007/s12046-022-01864-9

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