Abstract
Recognition of Devanagari ancient handwritten character is an important task for resourceful contents' exploitation of the priceless information contained in them. There are numerous Devanagari ancient handwritten documents from fifteenth to the nineteenth century. This paper presents an optical character recognition system for the recognition of Devanagari ancient manuscripts. In this paper, improved recognition results for Devanagari ancient characters have been presented using the scale-invariant feature transform (SIFT) and Gabor filter feature extraction techniques. Support vector machine (SVM) classifier is used for the classification task in this work. For experimental results, a database consisting of 5484 samples of Devanagari characters was collected from various ancient manuscripts placed in libraries and museums. SIFT- and Gabor filter-based features are used to extract the properties of the handwritten Devanagari ancient characters for recognition. Principle component analysis is used to reduce the length of the feature vector for reducing training time of the model and to improve recognition accuracy. Recognition accuracy of 91.39% has been achieved using the proposed system based on tenfold cross-validation technique and poly-SVM classifier.
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The authors declare that they have no conflict of interest. During our research, we suffered a lot from the lack of a public dataset. Thus, we don't have a benchmark to compare our algorithm with others. A public dataset may help other researchers working on similar projects as ours. So we decide to share our raw data for experimental work.
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Narang, S.R., Jindal, M.K., Ahuja, S. et al. On the recognition of Devanagari ancient handwritten characters using SIFT and Gabor features. Soft Comput 24, 17279–17289 (2020). https://doi.org/10.1007/s00500-020-05018-z
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DOI: https://doi.org/10.1007/s00500-020-05018-z