A Convolutional Recurrent Neural Network for the Handwritten Text Recognition of Historical Greek Manuscripts

Abstract

In this paper, a Convolutional Recurrent Neural Network architecture for offline handwriting recognition is proposed. Specifically, a Convolutional Neural Network is used as an encoder for the input which is a textline image, while a Bidirectional Long Short-Term Memory (BLSTM) network followed by a fully connected neural network acts as the decoder for the prediction of a sequence of characters. This work was motivated by the need to transcribe historical Greek manuscripts that entail several challenges which have been extensively analysed. The proposed architecture has been tested for standard datasets, namely the IAM and RIMES, as well as for a newly created dataset, namely EPARCHOS, which contains historical Greek manuscripts and has been made publicly available for research purposes. Our experimental work relies upon a detailed ablation study which shows that the proposed architecture outperforms state-of-the-art approaches.