Tigrinya OCR: Applying CRNN for Text Recognition

Tigrinya is a language predominantly spoken in Eritrea and the Tigray region of Ethiopia. It is classified as a low resourced language when it comes to Natural Language Processing (NLP) and its documents are not widely accessible due to the lack of printed material. Although the language has a rich cultural heritage, its literature hasn’t been exposed to large-scale automated digitization when compared to other widely-spoken languages. In this paper, we design an end-to-end CRNN (Convolutional Recurrent Neural Network) to recognize machine-printed Tigrinya text from document images. This will help Tigrinya documents to be more accessible and also bridge the gap with languages that are rich in NLP resources. We have included all the 304 characters in Tigrinya and the network is trained on a total of over a million text-line images constructed from different domains. The majority of the data was synthesized to augment the limited real data to help the model generalize better. We employed two external datasets (ADOCR and GLOCR) in addition to ours to train the network. Furthermore, to improve the performance of the model, extensive parameter tuning was conducted. Without the use of post processing techniques, the model has achieved a 2.32% Character Error Rate (CER). The learning curve shows that given more data, the model can improve the CER. We finally managed to get a lightweight model that achieves comparable results to state-of-the-art results. This result implies that augmenting low resource data with synthetic data can significantly reduce the error rate in text recognition and proper hyperparameter tuning can find us lightweight models without compromising much accuracy.