Digit/Symbol Pruning and Verification for Arabic Handwritten Digit/Symbol Spotting

Nobile, Nicola; He, Chun Lei; Sagheer, Malik Waqas; Lam, Louisa; Suen, Ching Y.

doi:10.1109/icdar.2011.136

Cited by 4 publications

(4 citation statements)

References 6 publications

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“…Each image is provided in gray scale as well as binarized form. The database has been employed in evaluation of symbol/digit recognition [159] as well as Farsi handwriting recognition [160,161].…”

Section: Cenparmi Farsi Databasementioning

confidence: 99%

A comprehensive survey of handwritten document benchmarks: structure, usage and evaluation

Hussain

Raza²,

Siddiqi

et al. 2015

J Image Video Proc.

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Handwriting has remained one of the most frequently occurring patterns that we come across in everyday life. Handwriting offers a number of interesting pattern classification problems including handwriting recognition, writer identification, signature verification, writer demographics classification and script recognition, etc. Research in these and similar related problems requires the availability of handwritten samples for validation of the developed techniques and algorithms. Like any other scientific domain, the handwriting recognition community has developed a large number of standard databases allowing development, evaluation and comparison of different techniques developed for a variety of recognition tasks. This paper is intended to provide a comprehensive survey of the handwriting databases developed during the last two decades. In addition to the statistics of the discussed databases, we also present a comparison of these databases on a number of dimensions. The ground truth information of the databases along with the supported tasks is also discussed. It is expected that this paper would not only allow researchers in handwriting recognition to objectively compare different databases but will also provide them the opportunity to select the most appropriate database(s) for evaluation of their developed systems.

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Section: Cenparmi Farsi Databasementioning

confidence: 99%

A comprehensive survey of handwritten document benchmarks: structure, usage and evaluation

Hussain

Raza²,

Siddiqi

et al. 2015

J Image Video Proc.

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“…Off CR [341], DR [342], WS [25] LMCA [343] 500 100,000 30,000 On WR, DR ADAB [344] 20,000+ Off Seg [344]- [346], Rec, On-WI [347] KHATT [348], [349] 1000 Off pre-processing, Seg, WI QUWI [350] 4068 Off WI [351], writer demographic classification [352]-[354] AHTID-MW [355] 3710 Off Seg [356], WI [357] IAUT/PHCN [358] Arabic(Farsi) 1140 34,200 Off pre-processing, WR [359]- [361] IFN Fars [327] Arabic(Farsi) 7271 Off DR, WR FHT [362] Arabic(Farsi) 1000 8050 106,600 Off Seg, Rec, BLD, content discrimination, WI, DLA CENPARMI-F [363] Arabic(Farsi) 432,357 Off DR [364], HR [365], [366] HaFT [367] Arabic(Farsi) 1800 Off Seg, Rec, WI CENPARMI-U [368] Arabic(Urdu) 18,000 Off HR [369], WS [370] UHSD [143] Arabic(Urdu) 400 Off Seg, Rec, WI PE92 [371] Korean 235,000 Off Rec [372] JPCD [373] Japanese 1227 Off Rec [215], [218] HCL2000 [374], [375] Chinese 3755 Off Rec [376] CASIA [377], [378] Chinese 1.35M On/Off Rec [379], WS [26] SCUT-COUCH [380], [381] Chinese 3.6M On Rec [382]-[384] CVL [385] Roman(Eng, Ger) 2163 Off WI [386], DR …”

Section: A Historical Document Datasetsmentioning

confidence: 99%

Document Analysis and Recognition: A survey

NIGAM¹,

Verma²,

Nagabhushan³

2023

Preprint

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<p>The journey of research for Document Analysis and Recognition (DAR) started with the problem of automatic character recognition. Today, it has covered a vast span of recognition tasks such as text recognition, script identification, writer identification, word spotting, signature verification etc., in scripts like Roman, Arabic, Chinese, Japanese, Brahmi etc. Extensive advancements in deep learning techniques have achieved state-of-the-art results for various DAR tasks. This work explores the challenges from different perspectives and reviews the techniques for online/offline and printed/handwritten DAR tasks. We examine the research works with the view of script-related challenges. Various datasets for DAR are categorized into historic, printed and handwritten datasets. We present a comprehensive survey of challenges, techniques, datasets, evaluation metrics, script-related perspectives and potential future directions for DAR.</p>

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“…They were standard HMM KWS applications without taking the particular properties of the Arabic script into account. A spotting scheme is developed specifically for Arabic handwritten digits/symbols achieved an overall precision of 80% and 83.3% recall [10]. Another prominent keyword spotting research conducted on both historical Arabic dataset VML and George Washington datasets.…”

Section: Related Workmentioning

confidence: 99%

“…Several methods have been proposed, and high identification accuracies are reported for the English handwritten digits [8,9]. Recently, researchers also proposed numeral spotting [10] and handwritten digit recognition systems for Arabic scripts on different datasets ( [11][12][13]). These studies achieved accuracies above 90%.…”

Section: Introductionmentioning

confidence: 99%

Automatic CNN-Based Arabic Numeral Spotting and Handwritten Digit Recognition by Using Deep Transfer Learning in Ottoman Population Registers

Can

Kabadayı

2020

Applied Sciences

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Historical manuscripts and archival documentation are handwritten texts which are the backbone sources for historical inquiry. Recent developments in the digital humanities field and the need for extracting information from the historical documents have fastened the digitization processes. Cutting edge machine learning methods are applied to extract meaning from these documents. Page segmentation (layout analysis), keyword, number and symbol spotting, handwritten text recognition algorithms are tested on historical documents. For most of the languages, these techniques are widely studied and high performance techniques are developed. However, the properties of Arabic scripts (i.e., diacritics, varying script styles, diacritics, and ligatures) create additional problems for these algorithms and, therefore, the number of research is limited. In this research, we first automatically spotted the Arabic numerals from the very first series of population registers of the Ottoman Empire conducted in the mid-nineteenth century and recognized these numbers. They are important because they held information about the number of households, registered individuals and ages of individuals. We applied a red color filter to separate numerals from the document by taking advantage of the structure of the studied registers (numerals are written in red). We first used a CNN-based segmentation method for spotting these numerals. In the second part, we annotated a local Arabic handwritten digit dataset from the spotted numerals by selecting uni-digit ones and tested the Deep Transfer Learning method from large open Arabic handwritten digit datasets for digit recognition. We achieved promising results for recognizing digits in these historical documents.

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Digit/Symbol Pruning and Verification for Arabic Handwritten Digit/Symbol Spotting

Cited by 4 publications

References 6 publications

A comprehensive survey of handwritten document benchmarks: structure, usage and evaluation

A comprehensive survey of handwritten document benchmarks: structure, usage and evaluation

Document Analysis and Recognition: A survey

Automatic CNN-Based Arabic Numeral Spotting and Handwritten Digit Recognition by Using Deep Transfer Learning in Ottoman Population Registers

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