Enhancing Hindi OCR robustness with CRNN-ResNet50: a data augmentation approach on the devanagari dataset.

Optical character recognition is a technology that turns texts and scanned documents into digital formats. The practical applications of OCR systems face a lot of challenges because of heterogeneity in scripts, font styles, and different quality images arising during their usage. The proposed work attempts to improve the robustness of OCR models built for the Hindi language by addressing the deficiencies inherent in the widely used IIIT-HW-Dev dataset. Toward this end, two methods of data augmentation are proposed: 1) synthetic images with half characters and conjuncts for complex words, and 2) various image degradations to simulate the real world. The augmented training set is used to train the two deep architectures, namely Convolutional Recurrent Neural Networks (CRNNs) and ResNet-50. Comparisons and in-depth experiments further reveal the merits of the suggested data augmentation techniques: the top performance obtained by the CRNN model exceeds current approaches. On the test set, CRNN achieved a Character Error Rate (CER) of 2.14% and Word Error Rate (WER) of 7.96%, much beyond the CER of 3.27% and WER of 11.82% achieved by the ResNet-50 model. Qualitatively, there was robustness in the case of complex words and images that were also deteriorated. The augmented dataset and trained models can be downloaded by anyone to promote more research in Hindi OCR. So, developing robust OCR systems for complex scripts starts from variability-rich and representative training data along with specific architectures, such as the CRNN architecture.