A novel method for slant correction of Persian handwritten digits and words

Nadi, Farhad; Sadri, Javad; Foroozandeh, Atefeh

doi:10.1109/pria.2013.6528443

Cited by 3 publications

(2 citation statements)

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“…All forms were scanned at 300 dpi and stored as binary images. The database has been mainly designed to support Farsi word recognition and preprocessing tasks [156][157][158].…”

Section: Iaut/phcn 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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“…All forms were scanned at 300 dpi and stored as binary images. The database has been mainly designed to support Farsi word recognition and preprocessing tasks [156][157][158].…”

Section: Iaut/phcn 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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