VML-HD: The historical Arabic documents dataset for recognition systems

Kassis, Majeed; Abdalhaleem, Alaa; Droby, Ahmad; Alaasam, Reem; El-Sana, Jihad

doi:10.1109/asar.2017.8067751

Cited by 33 publications

(15 citation statements)

References 15 publications

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“…VML-HD database [24]: This database includes 668 handwritten document images. The documents in the database were written in Arabic by different writers between the years 1088 and 1451.…”

Section: Review Of Related Public Datasetsmentioning

confidence: 99%

SHIBR—The Swedish Historical Birth Records: a semi-annotated dataset

Cheddad

Kusetoğulları

Hilmkil³

et al. 2021

Neural Comput & Applic

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This paper presents a digital image dataset of historical handwritten birth records stored in the archives of several parishes across Sweden, together with the corresponding metadata that supports the evaluation of document analysis algorithms’ performance. The dataset is called SHIBR (the Swedish Historical Birth Records). The contribution of this paper is twofold. First, we believe it is the first and the largest Swedish dataset of its kind provided as open access (15,000 high-resolution colour images of the era between 1800 and 1840). We also perform some data mining of the dataset to uncover some statistics and facts that might be of interest and use to genealogists. Second, we provide a comprehensive survey of contemporary datasets in the field that are open to the public along with a compact review of word spotting techniques. The word transcription file contains 17 columns of information pertaining to each image (e.g., child’s first name, birth date, date of baptism, father's first/last name, mother’s first/last name, death records, town, job title of the father/mother, etc.). Moreover, we evaluate some deep learning models, pre-trained on two other renowned datasets, for word spotting in SHIBR. However, our dataset proved challenging due to the unique handwriting style. Therefore, the dataset could also be used for competitions dedicated to a large set of document analysis problems, including word spotting.

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“…VML-HD database [24]: This database includes 668 handwritten document images. The documents in the database were written in Arabic by different writers between the years 1088 and 1451.…”

Section: Review Of Related Public Datasetsmentioning

confidence: 99%

SHIBR—The Swedish Historical Birth Records: a semi-annotated dataset

Cheddad

Kusetoğulları

Hilmkil³

et al. 2021

Neural Comput & Applic

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“…Non-latin scripts are becoming more and more relevant in the research on historical documents. VML-HD is a dataset of arabic documents [ 103 ]. It is based on five books written by various authors between 1088 and 1451.…”

Section: Experimental Environmentmentioning

confidence: 99%

Deep Learning for Historical Document Analysis and Recognition—A Survey

Lombardi

Marinai

2020

J. Imaging

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Nowadays, deep learning methods are employed in a broad range of research fields. The analysis and recognition of historical documents, as we survey in this work, is not an exception. Our study analyzes the papers published in the last few years on this topic from different perspectives: we first provide a pragmatic definition of historical documents from the point of view of the research in the area, then we look at the various sub-tasks addressed in this research. Guided by these tasks, we go through the different input-output relations that are expected from the used deep learning approaches and therefore we accordingly describe the most used models. We also discuss research datasets published in the field and their applications. This analysis shows that the latest research is a leap forward since it is not the simple use of recently proposed algorithms to previous problems, but novel tasks and novel applications of state of the art methods are now considered. Rather than just providing a conclusive picture of the current research in the topic we lastly suggest some potential future trends that can represent a stimulus for innovative research directions.

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“…A number of research groups have invested significant efforts in the creation and maintenance of annotated, publicly available historical manuscript image datasets [1]- [4], [11]- [13]. Other collections contain character-level and wordlevel spatial annotations for South-East Asian palm-leaf manuscripts [5], [10], [14].…”

Section: Related Workmentioning

confidence: 99%

Indiscapes: Instance Segmentation Networks for Layout Parsing of Historical Indic Manuscripts

Prusty

Aitha

Trivedi

et al. 2019

2019 International Conference on Document Analysis and Recognition (ICDAR)

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Historical palm-leaf manuscript and early paper documents from Indian subcontinent form an important part of the world's literary and cultural heritage. Despite their importance, large-scale annotated Indic manuscript image datasets do not exist. To address this deficiency, we introduce Indiscapes, the first ever dataset with multi-regional layout annotations for historical Indic manuscripts. To address the challenge of large diversity in scripts and presence of dense, irregular layout elements (e.g. text lines, pictures, multiple documents per image), we adapt a Fully Convolutional Deep Neural Network architecture for fully automatic, instance-level spatial layout parsing of manuscript images. We demonstrate the effectiveness of proposed architecture on images from the Indiscapes dataset. For annotation flexibility and keeping the non-technical nature of domain experts in mind, we also contribute a custom, webbased GUI annotation tool and a dashboard-style analytics portal. Overall, our contributions set the stage for enabling downstream applications such as OCR and word-spotting in historical Indic manuscripts at scale.

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VML-HD: The historical Arabic documents dataset for recognition systems

Cited by 33 publications

References 15 publications

SHIBR—The Swedish Historical Birth Records: a semi-annotated dataset

SHIBR—The Swedish Historical Birth Records: a semi-annotated dataset

Deep Learning for Historical Document Analysis and Recognition—A Survey

Indiscapes: Instance Segmentation Networks for Layout Parsing of Historical Indic Manuscripts

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