Subword Recognition in Historical Arabic Documents using C-GRUs

Abstract

The recent years have witnessed an increased tendency to digitize historical manuscripts that not only ensures the preservation of these collections but also allows researchers and end-users’ direct access to these images. Recognition of Arabic handwriting is challenging due to the highly cursive nature of the script and other challenges associated with historical documents (degradation etc.). This paper presents an end-to-end system to recognize Arabic handwritten sub words in historical documents. More specifically, we introduce a hybrid CNN-GRU model where the shallow convolutional network learns robust feature representations while the GRU layers carry out the sequence modelling and generate the transcription of the text. The proposed system is evaluated on two different datasets, IBN SINA and VML-HD reporting recognition rates of 96.10% and 98.60% respectively. A comparison with existing techniques evaluated on the same datasets validates the effectiveness of our proposed model in characterizing Arabic subwords.

Character Recognition of Arabic Handwritten Characters Using Deep Learning

“…El-Sawy et all trained the model on the AHCD dataset The model has two layers' normalization and employing dropout. The CNN model achieved an accuracy of 94.9%.Hassan et al[14] designed a CNN architecture with two fully connected layers for recognizing historical Arabic handwrite texts. They tested their CNN model on handwriting text images historical, text image synthesized database, and text print image database and they achieved an accuracy of 85%.…”