Abstract
Script identification in multi-lingual text images will help in improving the efficiency of many real life applications, such as sorting, transcription of multilingual documents and OCR. In this paper, we have presented a technique for identification of three scripts, namely, Devanagari, Gurmukhi and Roman. We have identified the script of text based on statistical features, namely, zoning features; diagonal features; intersection and open end points based features; peak extent based features and combinations of these features. For classification, we have used multiple classification techniques, namely, Support Vector Machine (SVM), k-Nearest Neighbour (k-NN), and Convolutional Neural Network (CNN). The proposed strategy using CNN attains an average identification rate of 93.64%, with 5-fold cross-validation, for these three scripts when isolated offline handwritten characters of these scripts were considered.
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Bhardwaj, A., Jindal, S.R. (2017). Script Identification from Offline Handwritten Characters Using Combination of Features. In: Deep, K., et al. Proceedings of Sixth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 547. Springer, Singapore. https://doi.org/10.1007/978-981-10-3325-4_17
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DOI: https://doi.org/10.1007/978-981-10-3325-4_17
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