Learning by Injection: Attention Embedded Recurrent Neural Network for Amharic Text-image Recognition

In the present, the growth of digitization and worldwide communications make2 OCR systems of exotic languages a very important task. In this paper, we develop an OCR model for one of these exotic languages with a unique script, Amharic. Motivated with the recent success of the Attention mechanism in Neural Machine Translation (NMT), we extend the attention mechanism for Amharic text-image recognition. The proposed model consists of CNNs and attention embedded encoder-decoder networks that are integrated following the configuration of the seq2seq framework. Unlike the existing OCR model that minimizes the CTC objective function, the new model minimizes the categorical cross-entropy loss. The performance of the proposed attention-based model is evaluated against the test dataset from the ADOCR database which consists of both printed and synthetically generated Amharic text-line images, and achieved a promising result with a 13 Character Error Rate (CER) of 1.54% and 1.17% respectively.