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Performance optimization for handwritten Gujarati alphanumeric script identification

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Abstract

Human development analyses have revealed that only a significant fraction of Indians can write and read the English language. It outlines the necessity of conducting research for Indian scripts based on OCR technology. Integration of an autonomous OCR system that identifies handwritten texts from documents can improve the user experience by offering quick access to details, allowing for revisions, and consuming less storage space. There are numerous unresolved difficult issues for research in Indian scripts like Gujarati, which is still in its infancy due to a large number of consonants, vowels, vowel modifiers, and compound characters. As a result, its major issue is achieving a high recognition rate. The performance of handwritten Gujarati character identification using deep learning and machine learning approaches is improved by the investigation of a unique fusion strategy. The suggested strategies were tested over the handwritten Gujarati alphanumeric datasets. One of the recommended hybrid strategies, MobileNet-SVM, surpassed all other methodologies, obtaining a 97.29% recognition accuracy.

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Acknowledgements

The authors would like to express their sincere appreciation to the Institute of Technology, Nirma University, for their valuable support and insights during the 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. Performance optimization for handwritten Gujarati alphanumeric script identification. Sādhanā 48, 250 (2023). https://doi.org/10.1007/s12046-023-02307-9

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  • DOI: https://doi.org/10.1007/s12046-023-02307-9

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