A New Approach for Segmentation and Recognition of Arabic Handwritten Touching Numeral Pairs

Alamri, Huda; He, Chun Lei; Suen, Ching Y.

doi:10.1007/978-3-642-03767-2_20

Cited by 12 publications

(7 citation statements)

References 8 publications

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“…The third set is a combination of samples from set 1 and set 2. The database has been used for recognition of Arabic characters [140] and numerals [141] as well as word spotting [142]. …”

Section: Cenparmi Arabic Handwriting 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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“…The third set is a combination of samples from set 1 and set 2. The database has been used for recognition of Arabic characters [140] and numerals [141] as well as word spotting [142]. …”

Section: Cenparmi Arabic Handwriting 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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“…Since no similar work in the field of Persian digits segmentation has been done before, no comparison could be made with the previous works. However, a recent article [24] which has dealt with connected Arabic numerals has taken advantage of the characteristics of gradient, the segmentation rate of which for Arabic digits has at its best been 92.2 %.…”

Section: The Results Based On the Experimentsmentioning

confidence: 98%

A method for the segmentation of connected handwritten Persian digits

Ashtiany

Faez

2010

2010 11th International Conference on Control Automation Robotics &Amp; Vision

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This article presents in two modules a new method for segmenting connected handwritten Persian digits using the characteristics of the foreground and utilizing the background skeleton.The first module excavates all the valleys and hills , if there are any ,from the upper pixels and lower pixels of the thinned image respectively. Then feature point excavate. For better segmentation the digits , a separability degree is calculated, regarding the height of the hills and valleys close to each feature point considering the characteristic of connected Persian digits. Then the significance degree is calculated to determine of its influence rate in segmentation. Then, using that, one or few points with high significance degree, which are more influence in the segmentation, are selected as the cutting points.Having excavated the background skeleton, the second module begins to identify the priority points in the skeleton in order to connect them to the cutting points The conducted experiments confirm the accuracy of the factors utilized and the results indicates a correct segmentation at a rate of 97.2%.

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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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A New Approach for Segmentation and Recognition of Arabic Handwritten Touching Numeral Pairs

Cited by 12 publications

References 8 publications

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

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

A method for the segmentation of connected handwritten Persian digits

Document Analysis and Recognition: A survey

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