Optical Character Recognition using Convolutional Neural Networks for Ashokan Brahmi Inscriptions
This work addresses the preservation and digitization of ancient scripts in epigraphy, but it is incremental as it applies existing methods to a new dataset.
The paper tackled the problem of recognizing Ashokan Brahmi characters from inscriptions by developing an OCR system using pre-trained convolutional neural networks, with MobileNet achieving the best result of 95.94% validation accuracy and 0.129 validation loss.
This research paper delves into the development of an Optical Character Recognition (OCR) system for the recognition of Ashokan Brahmi characters using Convolutional Neural Networks. It utilizes a comprehensive dataset of character images to train the models, along with data augmentation techniques to optimize the training process. Furthermore, the paper incorporates image preprocessing to remove noise, as well as image segmentation to facilitate line and character segmentation. The study mainly focuses on three pre-trained CNNs, namely LeNet, VGG-16, and MobileNet and compares their accuracy. Transfer learning was employed to adapt the pre-trained models to the Ashokan Brahmi character dataset. The findings reveal that MobileNet outperforms the other two models in terms of accuracy, achieving a validation accuracy of 95.94% and validation loss of 0.129. The paper provides an in-depth analysis of the implementation process using MobileNet and discusses the implications of the findings. The use of OCR for character recognition is of significant importance in the field of epigraphy, specifically for the preservation and digitization of ancient scripts. The results of this research paper demonstrate the effectiveness of using pre-trained CNNs for the recognition of Ashokan Brahmi characters.