An Historical Handwritten Arabic Dataset for Segmentation-Free Word Spotting - HADARA80P

Pantke, Werner; Dennhardt, Martin; Fecker, Daniel; Märgner, Volker; Fingscheidt, Tim

doi:10.1109/icfhr.2014.11

Cited by 16 publications

(8 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among the methods that are based on the Word Spotting technique we can mention the HADARA80P method [44] which uses a dataset of 80 pages available on the internet [45] including images in Tiff format at high resolution. The documents handled are 9th century Arabic manuscripts.…”

Section: Resultsmentioning

confidence: 99%

A Word Spotting Method for Arabic Manuscripts Based on Speeded Up Robust Features Technique

Makhfi¹

2019

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

The diversity of manuscripts according to their contents, forms, organizations and presentations provides a data-rich structures. The aim is to disseminate this cultural heritage in the images format to the general public via digital libraries. However, handwriting is an obstacle to text recognition algorithms in images, especially cursive writing of Arabic calligraphy. Most current search engines used by digital libraries are based on metadata and structured data manually transcribed in Ascii format. In this article, we propose an original method of pattern recognition for searching the content of Arabic handwritten documents based on the Word Spotting technique. Our method is both effective and simple, it consists in extracting a set of features from the words we segment in the target images and comparing them with the features of the words in the requested images. The principle of the method is to characterize each word with the Speeded Up Robust Features algorithm whose goal is to find all occurrences of query words in the target image even in the case of low-resolution images. We tested our method on hundreds of pages of Arabic manuscripts from the National Library of Rabat and the Digital Library of Leipzig University. The results obtained are encouraging compared to other methods based on the same Word Spotting technique.

show abstract

Section: Resultsmentioning

confidence: 99%

A Word Spotting Method for Arabic Manuscripts Based on Speeded Up Robust Features Technique

Makhfi¹

2019

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

show abstract

“…The datasets are sorted from earliest to latest release year. Writer Order GERMANA [122] 3.3.1 handwritten RODRIGO [135] 3.3.2 handwritten IAM-HistDB [47] 3.3.3 handwritten PHTD [2] 3.2.1 handwritten PBOK [5] 3.2.2 handwritten IMPACT [117] 3.2.3 both ESPOSALLES [128] 3.3.4 handwritten BH2M [44] 3.3.5 handwritten HADARA80P [116] 3.3.6 handwritten ENP [29] 3.2.4 printed GRPOLY-DB [55] 3.2.5 both DocExplore [41] 3.3.7 both DIVA-HisDB [144] 3.2.6 handwritten AMADI_LontarSet [77] 3.3.8 handwritten ICFHR16 CLaMM [33] 3.1.1 handwritten ICDAR17 CLaMM [34] 3.1.2 handwritten HBA 1.0 [101] 3.2.7 both SleukRith [153] 3.3.9 handwritten VML-HD [76] 3.3.10 handwritten CFRAMUZ [10] 3.3.11 handwritten Lontar Sunda [148] 3.3.12 handwritten ICDAR17 REID2017 [30] 3.3.13 printed ICDAR17 Historical-WI [45] 3.3.14 handwritten Kuzushiji [27] 3.3.15 printed READ-BAD [60] 3.2.8 printed Warped Arabic [40] 3.2.9 both MHDID [140] 3.3.16 handwritten ( ) Tripitaka [161] 3.3.17 handwritten KERTAS [1] 3.1.3 handwritten ICFHR18 RASM2018 [31] 3.3.18 handwritten ICFHR18 Asian Palm Leaf [78] 3.3.19 handwritten ARDIS [84] 3.3.20 handwritten Pinkas [83] 3.2.10 handwritten BADAM [79] 3.2.11 handwritten HORAE [14] 3.2.12 handwritten ICDAR19 cTDaR19 [52] 3.2.13 handwritten ICDAR19 DMAS2019 4 3.2.14 printed OBC306 [67] 3.3.21 han...…”

Section: Datasetsmentioning

confidence: 99%

A Survey of Historical Document Image Datasets

Nikolaidou¹,

Seuret²,

Mokayed³

et al. 2022

Preprint

View full text Add to dashboard Cite

This paper presents a systematic literature review of image datasets for document image analysis, focusing on historical documents, such as handwritten manuscripts and early prints. Finding appropriate datasets for historical document analysis is a crucial prerequisite to facilitate research using different machine learning algorithms. However, because of the very large variety of the actual data (e.g., scripts, tasks, dates, support systems, and amount of deterioration), the different formats for data and label representation, and the different evaluation processes and benchmarks, finding appropriate datasets is a difficult task. This work fills this gap, presenting a meta-study on existing datasets. After a systematic selection process (according to PRISMA guidelines), we select 56 studies that are chosen based on different factors, such as the year of publication, number of methods implemented in the article, reliability of the chosen algorithms, dataset size, and journal outlet. We summarize each study by assigning it to one of three pre-defined tasks: document classification, layout structure, or semantic analysis. We present the statistics, document type, language, tasks, input visual aspects, and ground truth information for every dataset. In addition, we provide the benchmark tasks and results from these papers or recent competitions. We further discuss gaps and challenges in this domain. We advocate for providing conversion tools to common formats (e.g., COCO format for computer vision tasks) and always providing a set of evaluation metrics, instead of just one, to make results comparable across studies.

show abstract

“…HADARA80P database [25]: This database contains 80 handwritten document images written in the Arabic language. This database is used for word segmentation problems, as the words in the document images are annotated with polygons.…”

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

View full text Add to dashboard Cite

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.

show abstract

An Historical Handwritten Arabic Dataset for Segmentation-Free Word Spotting - HADARA80P

Cited by 16 publications

References 12 publications

A Word Spotting Method for Arabic Manuscripts Based on Speeded Up Robust Features Technique

A Word Spotting Method for Arabic Manuscripts Based on Speeded Up Robust Features Technique

A Survey of Historical Document Image Datasets

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

Contact Info

Product

Resources

About