Document Image Script Identification Using Deep Network

In this paper, we propose script identification of Indian language document images using MobileNetV3. Script identification at the page level is an essential task for Optical Character Recognition (OCR). OCR is a time-consuming task with a lot of computation required. To make an accurate, fast, and efficient script identification module, we have employed MobileNetV3, a mobile and CPU-friendly Convolutional Neural Network (CNN) algorithm. MobileNetV3 results are comparable to ResNet50 on the ImageNet dataset. The methodology addresses challenges in real-world scenarios, enhancing robustness. The paper demonstrates the classification of six Indian scripts (Bangla, Gurumukhi, Hindi, Kannada, Malayalam, and Tamil) and English. Four experiments have been performed. The first experiment uses MobileNetV3 with center loss (CL) and without skew detection (no angle). The second experiment uses MobileNetV3 without CL and skew detection (no angle). The third experiment is done by modifying MobileNetV3 with CL and without skew. The fourth experiment uses MobileNetV3 with CL and skew angle detection (range −10 to 10 degrees). The accuracy of the fourth experiment is 96% with skew detection. The best experiment is the first, which uses CL and cross-entropy loss and achieves an impressive accuracy of 98%.