Tamil Handwritten City Name Database Development and Recognition for Postal Automation

Thadchanamoorthy, S.; Kodikara, Nihal; Premaretne, H. L.; Pal, Umapada; Kimura, Fumitaka

doi:10.1109/icdar.2013.162

Cited by 25 publications

(5 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Highest recognition accuracy of 90.8% is obtained with the help of SVM classi er. Similarly, adchanamorthy et al [7] have introduced a technique to recognize city names which are written in Tamil Script. Database having 265 Tamil handwritten city names is created rst.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Optimized CNN‐Based Recognition of District Names of Punjab State in Gurmukhi Script

Sharma

Gupta

et al. 2022

Journal of Mathematics

View full text Add to dashboard Cite

Automation of Postal systems has the major research scope in the field of automation. To create Postal Automation set-up for countries like India is a tedious task if compared with other countries because of India’s multiscript and multilingual behavior. This work will help in recognizing the “Gurmukhi” handwritten district names of the State Punjab. To recognize the district names, a CNN-based architecture is proposed by employing a Holistic approach. For this, an image database of 22000 samples is prepared having 1000 sample images for every district name which is collected from 500 different writers. Maximum accuracy on validation data achieved by the proposed Model is 99%.

show abstract

Section: Related Workmentioning

confidence: 99%

[Retracted] Optimized CNN‐Based Recognition of District Names of Punjab State in Gurmukhi Script

Sharma

Gupta

et al. 2022

Journal of Mathematics

View full text Add to dashboard Cite

show abstract

“…A database of handwritten city names in Tamil, a popular script in India and Sri Lanka, is presented in [202]. The database includes a total of 265 different city names with 109 cities from Indian state of Tamil Nadu and 156 cities from Sri Lanka.…”

Section: A Database Of Tamil Handwritten City Namesmentioning

confidence: 99%

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

Hussain

Raza²,

Siddiqi

et al. 2015

J Image Video Proc.

View full text Add to dashboard Cite

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.

show abstract

“…On CR [213] Gujarati DB [211] Gujarati 4500 3700 1000 On CR [211] KHTD [394] Kannada 204 4000 26,000 Off Seg, Rec Tamil DB [395] Tamil 265,00 Off Rec uTHCD [396] Tamil 91000 265,00 Off Preprocessing [397], Rec [398] HPL-iso-Tamil-char [399] Tamil 78000 Off CR HPL-iso-Tamil-word [400] Tamil 8500 Off WR Tamil-Kannada word corpus [401] Tamil, Kannada 100000Ka, 100000Ta…”

Section: A Historical Document Datasetsmentioning

confidence: 99%

Document Analysis and Recognition: A survey

NIGAM¹,

Verma²,

Nagabhushan³

2023

Preprint

View full text Add to dashboard Cite

<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>

show abstract

Tamil Handwritten City Name Database Development and Recognition for Postal Automation

Cited by 25 publications

References 9 publications

[Retracted] Optimized CNN‐Based Recognition of District Names of Punjab State in Gurmukhi Script

[Retracted] Optimized CNN‐Based Recognition of District Names of Punjab State in Gurmukhi Script

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

Document Analysis and Recognition: A survey

Contact Info

Product

Resources

About