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Mallappa, Satishkumar
- Script Identification from Camera Captured Indian Document Images with CNN Model
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Authors
Affiliations
1 Department of Mathematical and Computational Sciences, Sri Sathya Sai University for Human Excellence Kalaburagi Campus, IN
2 Department of Computer Science, Garden City University, IN
3 Department of Computer Science, Central University of Karnataka, IN
1 Department of Mathematical and Computational Sciences, Sri Sathya Sai University for Human Excellence Kalaburagi Campus, IN
2 Department of Computer Science, Garden City University, IN
3 Department of Computer Science, Central University of Karnataka, IN
Source
ICTACT Journal on Soft Computing, Vol 14, No 2 (2023), Pagination: 3232-3236Abstract
Compared to typical scanners, handheld cameras offer convenient, flexible, portable, and noncontact image capture, which enables many new applications and breathes new life into existing ones, but camera-captured documents may suffer from distortions caused by a nonplanar document shape and perspective projection, which lead to the failure of current optical character recognition (OCR) technologies. This paper presents a new CNN model for script identification from camera-captured Indian multilingual document images. To evaluate the performance of the proposed model 9 regional languages, one national language and one international Roman languages are considered. Two languages, Hindi national language, and Roman English language are taken as the common languages with regional language for the study. The proposed method is applied on Bi-script, Tri-script, and Multi-script combinations. The average recognition accuracy for three script combinations is 92.92%, for bi-script 91.33%, and for tri-script 87.33%. is achieved. The proposed method is the unified approach used for identifying the script from bi-script, tri-script and multi-script camera-captured document images and is the novelty of this paper. The proposed model is compared with the Alexnet pretrained CNN model, and it achieved the highest recognition accuracy.Keywords
OCR, Deep Neural Network, Alexnet, CNN, Script Identification.
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