Line Level Modi (Heritage script) OCR using Attention based Encoder-Decoder Architecture

A large number of historical documents exist in the Modi script. These documents need to be preserved and digitized so that people know more about history written in Modi. Very little work has been done for the digitization of the Modi script documents. Deep learning-based solutions help to generate text output and reduce the digitization time for Modi documents. Most of the work happened only at the character level in the Modi OCR (Optical Character Recognition). This is the first effort to recognize the line images of the Modi script.Modi OCR has three major challenges. First, there are no word boundaries in Modi script like the Marathi language. All the words are connected so word length is more as compared to other Indian languages. Second, Modi documents are mainly handwritten. Third, Text is written on plain documents which make line segmentation more difficult.This paper aims to solve the first problem with the deep neural network. We do not have a real annotated dataset for the Modi script. We generate 303571 Modi line images synthetically with noise and blur for this experiment using in-house developed MODI_SAHO font. This paper describes the usage of OpenNMT architecture for recognition due to the flexibility of hyperparameter tuning. We use a neural encoder-decoder model with the attention mechanism for the Modi OCR. We evaluated the text recognition performance on synthetically generated line images. Our method is evaluated on the basis of the text output of line images. The accuracy of the test set is achieved 99.97%. Our major contribution is to show text recognization of very long words with the encoder-decoder architecture. Our contribution helps the researcher community in building long connected words OCR.