Marwari (Heritage Script) OCR Using Attention Based Encoder-Decoder Architecture

The Marwari language is home to a vast collection of historical handwritten documents that require preservation and digitalization to deepen our understanding of Marwari history. Unfortunately, the digitization of Marwari language documents has seen limited progress thus far. To address this, deep learning techniques offer valuable solutions for text generation and expediting the digitization process. This study pioneers the recognition of printed line images in the Marwari and tackles three major challenges in Marwari OCR. Firstly, unlike Hindi, the Marwari language lacks distinct word boundaries, resulting in longer interconnected words compared to other Indian languages. Moreover, handwritten form is the predominant format for Marwari documents. Lastly, the presence of plain documents adds to the complexity of line segmentation, as the text is often written without any specific formatting or structure. In this paper, we propose a novel approach utilizing DNN to overcome the first challenge. Due to the absence of an actual annotated dataset, 74,339 synthetic Marwari line images are generated with augmentation using in-house developed Bikaner font. Our method harnesses the CNN-Transformer architecture, chosen for its flexibility in hyperparameter tuning, for text recognition. The model takes Marwari line images as input, utilizing a CNN to extract visual features, which are then fed to the transformer decoder for text generation. We evaluate our approach using an encoder based on ResNet18 and a transformer decoder, achieving an impressive accuracy of 99.53% on the test set. The primary contribution of this work lies in demonstrating the successful recognition of extremely lengthy interconnected words which will benefit the research community.