Holistic Urdu Handwritten Word Recognition Using Support Vector Machine

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

doi:10.1109/icpr.2010.468

Cited by 45 publications

(25 citation statements)

References 10 publications

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“…The lexicon of 57 Urdu words and 44 Urdu characters mainly comprises financial terms to support recognition of offline Urdu words, characters and digits. This is the first published database on Urdu handwriting and has been employed in recognition and spotting of Urdu handwritten words [165,166].…”

Section: Cenparmi Urdu 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 Urdu 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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show abstract

“…Another categorization of these OCRs is based on units of text used for recognition. Some of the techniques work on isolated characters only [2], [4], [5], [6] while others work on complete words or ligatures [7], [8], [9], [10], [11]. The systems developed to work with isolated characters naturally report much better results as opposed to those working on words or ligatures.…”

Section: Introductionmentioning

confidence: 99%

“…A segmentation-free approach for recognition of Urdu handwritten words is proposed in [9]. The authors extract gradient and structural features from Urdu words which are recognized using Support Vector Machine with radial basis function (RBF) as kernel.…”

Section: Introductionmentioning

confidence: 99%

Optical Character Recognition System for Urdu Words in Nastaliq Font

Shabbir¹,

Siddiqi²

2016

ijacsa

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Abstract-Optical Character Recognition (OCR) has been an attractive research area for the last three decades and mature OCR systems reporting near to 100% recognition rates are available for many scripts/languages today. Despite these developments, research on recognition of text in many languages is still in its early days, Urdu being one of them. The limited existing literature on Urdu OCR is either limited to isolated characters or considers limited vocabularies in fixed font sizes. This research presents a segmentation free and size invariant technique for recognition of Urdu words in Nastaliq font using ligatures as units of recognition. Ligatures, separated into primary ligatures and diacritics, are recognized using right-to-left HMMs. Diacritics are then associated with the main body using position information and the resulting ligatures are validated using a dictionary. The system evaluated on Urdu words realized promising recognition rates at ligature and word levels.

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“…The ligatures themselves have to be extracted from text lines but since they are not further segmented, these techniques are termed as segmentationfree. Holistic techniques are known to be more robust for Urdu text as reported in a number of studies [5,9,10,12,19,[27][28][29][30][31][32].…”

Section: Holistic Approachesmentioning

confidence: 99%

Segmentation-free optical character recognition for printed Urdu text

Din

Siddiqi

Khalid

et al. 2017

J Image Video Proc.

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This paper presents a segmentation-free optical character recognition system for printed Urdu Nastaliq font using ligatures as units of recognition. The proposed technique relies on statistical features and employs Hidden Markov Models for classification. A total of 1525 unique high-frequency Urdu ligatures from the standard Urdu Printed Text Images (UPTI) database are considered in our study. Ligatures extracted from text lines are first split into primary (main body) and secondary (dots and diacritics) ligatures and multiple instances of the same ligature are grouped into clusters using a sequential clustering algorithm. Hidden Markov Models are trained separately for each ligature using the examples in the respective cluster by sliding right-to-left the overlapped windows and extracting a set of statistical features. Given the query text, the primary and secondary ligatures are separately recognized and later associated together using a set of heuristics to recognize the complete ligature. The system evaluated on the standard UPTI Urdu database reported a ligature recognition rate of 92% on more than 6000 query ligatures.

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Holistic Urdu Handwritten Word Recognition Using Support Vector Machine

Cited by 45 publications

References 10 publications

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

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

Optical Character Recognition System for Urdu Words in Nastaliq Font

Segmentation-free optical character recognition for printed Urdu text

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