Densely connected layer to improve VGGnet-based CRNN for Arabic handwriting text line recognition

Abstract

In recent years, Deep neural networks (DNNs) have achieved great success in sequence modeling. Several deep models have been used for enhancing Handwriting Text Recognition (HTR). Among these models, Convolutional Neural Networks (CNNs) and Recurrent Neural network especially Long-Short-Term-Memory (LSTM) networks achieve state-of-the-art recognition accuracy. The recognition methods for Arabic text lines have been widely applied in many specific tasks. However, there are still some potential challenges as the lack of available and large Arabic text recognition dataset and the characteristics of Arabic script. In order to address these challenges, we propose an end-to-end recognition method based on convolutional recurrent neural networks (CRNNs), which adds feature reuse network component on the basis of a CRNN. The model is trained and tested on two Arabic text recognition datasets named KHATT and AHTID/MW. The experimental results demonstrate that the proposed method achieves better performance than other methods in the literature.