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Machine learning analysis demonstrates accurate character recognition in ancient Devanagari manuscripts, highlighting the efficacy of transfer learning for cultural heritage digitization.
Ancient manuscripts are one of the major source of obtaining valuable information. Reading these manuscripts is a tedious task so there came a requirement of digitization of these images. Many such OCRs exist which can decode the manuscripts and convert them into a human readable form. One such OCR is developed in this paper which was able to read regional language-Devanagari. For this classification AlexNet transfer learning model is used. In this paper Devanagari manuscript data set is used to classify the Devanagari characters. A total of 7356 characters were included in the dataset which was a combination of Devanagari basic characters and some modifiers and conjuncts. The experiments were performed after dividing the dataset into 90–10 (P1), 80–20 (P2) and 70-30(P3) train-test data. A maximum accuracy of 95.4% was obtained when the model was made to run on P1 partitioning strategy on 30 epochs. The average accuracy of 93.3% obtained for AlexNet model. Also, this paper compares the performance of other CNN based OCRs.
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Moudgil et al. (2024) studied this question.
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