Subword Recognition in Historical Arabic Documents using C-GRUs

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 na...

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Autores principales:	Hanadi Hassen, Somaya Al-Madeed, Ahmed Bouridane
Formato:	article
Lenguaje:	EN
Publicado:	UIKTEN 2021
Materias:	handwriting recognition arabic historical documents cnns grus classification Education L Technology T
Acceso en línea:	https://doaj.org/article/ca1500247a4d4c0881175e0aacbae990
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id	oai:doaj.org-article:ca1500247a4d4c0881175e0aacbae990
record_format	dspace
spelling	oai:doaj.org-article:ca1500247a4d4c0881175e0aacbae9902021-12-01T10:50:02ZSubword Recognition in Historical Arabic Documents using C-GRUs10.18421/TEM104-192217-83092217-8333https://doaj.org/article/ca1500247a4d4c0881175e0aacbae9902021-11-01T00:00:00Zhttps://www.temjournal.com/content/104/TEMJournalNovember2021_1630_1637.pdfhttps://doaj.org/toc/2217-8309https://doaj.org/toc/2217-8333The 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Arabic handwriting is challenging due to the highly cursive nature of the script and other challenges associated with historical documents (degradation etc.). This paper presents an end-to-end system to recognize Arabic handwritten sub words in historical documents.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.Hanadi HassenSomaya Al-MadeedAhmed BouridaneUIKTENarticlehandwriting recognitionarabic historical documentscnnsgrusclassificationEducationLTechnologyTENTEM Journal, Vol 10, Iss 4, Pp 1630-1637 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	handwriting recognition arabic historical documents cnns grus classification Education L Technology T
spellingShingle	handwriting recognition arabic historical documents cnns grus classification Education L Technology T Hanadi Hassen Somaya Al-Madeed Ahmed Bouridane Subword Recognition in Historical Arabic Documents using C-GRUs
description	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.
format	article
author	Hanadi Hassen Somaya Al-Madeed Ahmed Bouridane
author_facet	Hanadi Hassen Somaya Al-Madeed Ahmed Bouridane
author_sort	Hanadi Hassen
title	Subword Recognition in Historical Arabic Documents using C-GRUs
title_short	Subword Recognition in Historical Arabic Documents using C-GRUs
title_full	Subword Recognition in Historical Arabic Documents using C-GRUs
title_fullStr	Subword Recognition in Historical Arabic Documents using C-GRUs
title_full_unstemmed	Subword Recognition in Historical Arabic Documents using C-GRUs
title_sort	subword recognition in historical arabic documents using c-grus
publisher	UIKTEN
publishDate	2021
url	https://doaj.org/article/ca1500247a4d4c0881175e0aacbae990
work_keys_str_mv	AT hanadihassen subwordrecognitioninhistoricalarabicdocumentsusingcgrus AT somayaalmadeed subwordrecognitioninhistoricalarabicdocumentsusingcgrus AT ahmedbouridane subwordrecognitioninhistoricalarabicdocumentsusingcgrus
_version_	1718405260861505536

Subword Recognition in Historical Arabic Documents using C-GRUs

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