Subword Recognition in Historical Arabic Documents using C-GRUs

Hanadi Hassen*, Somaya Al-Madeed, Ahmed Bouridane

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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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Arabic handwriting is challenging due to the highly cursive nature of the script and other challenges associated with historical documents (degradation etc.). This paper presents an end-to-end system to recognize Arabic handwritten sub words in historical documents.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.

Original languageEnglish
Pages (from-to)1630-1637
Number of pages8
JournalTEM Journal
Issue number4
Publication statusPublished - 26 Nov 2021


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