Ligature based optical character recognition of Urdu- Nastaleeq font

Shah, Zehra

doi:10.1109/inmic.2002.1310132

Cited by 20 publications

(9 citation statements)

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“…On the other hand, segmentation of ligature into character has several issue due to the complexity of Arabic script. In 2002, Shah and Saleem [7] proposed a method of the connected components and the baseline using horizantal profile for segmentation of words into ligatures. This approach helps in separation of primary ligatures and secondar ligatures from each other.…”

Section: Segmentation Methods For Urdu Recognitionmentioning

confidence: 99%

Arabic script based character segmentation: A review

Naz

Hayat

Razzak

et al. 2013

2013 World Congress on Computer and Information Technology (WCCIT)

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Segmentation based Arabic script based languages character recognition has been a popular field of research for many years. The challenging nature of Arabic script recognition has attracted the attention of researchers from both industry and academic circles but these efforts have not achieved good results until now. Segmentation of Urdu script when written in Nasta'liq writing style is very difficult task due to the complexity of writing style as compare to Naskh writing style. Good segmentation is one of reasons for high accuracy. Character segmentation has been a critical phase of the OCR process. The higher recognition rates for isolated characters as compare to results of words or connected character well illustrate the importance of segmentation. Current study investigate the recent work for character segmentation and challenges for segmentation for Arabic script based languages.

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Section: Segmentation Methods For Urdu Recognitionmentioning

confidence: 99%

Arabic script based character segmentation: A review

Naz

Hayat

Razzak

et al. 2013

2013 World Congress on Computer and Information Technology (WCCIT)

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“…These include the number of dots present in the character, place of the dot, branch or presence of secondary stroke, aspect ratio and slope between the initial point and the end point. Structural features also play an active role in the technique employed in [12], in which visible features, such as the location of the dots and placement of other diacritics are extracted for every ligature. Structural features, such as character lengths, number and position of loops or holes, and distance between two consecutive lines have also been extracted by [13] in their research.…”

Section: Feature Extractionmentioning

confidence: 99%

Offline Recognition of Handwritten Urdu Characters using B Spline Curves: A Survey

Jameel¹,

Kumar²

2017

IJCA

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Handwritten Character Recognition is an active area of research in the field of pattern recognition and image processing for last two decades as there is an urgent need of having a successful Script Recognition System to convert handwritten documents into computer understandable form which is applicable for various purposes. Several research studies have been carried out for recognition of other scripts like Chinese, Japanese, English, Devanagari, etc. but the research regarding Urdu Script is still immature due to cursive and variable nature of Urdu characters. The requirement of offline Urdu HCR systems is increasing because of the expansion of technology and the convenience for users. In this paper, a detailed survey of Urdu HCR techniques with respect to feature extraction developed so far alongwith their efficiency and accuracy has been presented. The paper also presents a new proposed B-Spline Curve approximation approach for feature extraction of offline isolated Urdu handwritten characters.

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“…Structural features are typically computed by finding the extreme points and joining points [25] or considering the number of dots, position of the dots, presence of branches, loops or secondary strokes and the slope between the initial point and the final point [26,32]. Statistical features, for which rich classifiers are available, are mostly preferred over structural features and a large number of techniques rely on statistical features including shape descriptors, contour based statistics, edge based features and other statistical measurements computed at word, ligature or character levels [26][27][28][29][30].…”

Section: Related Workmentioning

confidence: 99%

“…Challenges in recognition of Nasta'liq Urdu text include diagonality, multiple baselines, high cursiveness and context sensitivity. Most of the studies on recognition of Urdu text use ligatures as the basic unit of recognition [31][32][40][41][42][43]. The total number of unique Urdu ligatures is approximately 22,000 [39] and training classifiers to learn to discriminate such a large number of classes is a challenging task.…”

Section: Introductionmentioning

confidence: 99%

An Ocr system for printed Nasta'liq script: A segmentation based approach

Naz

Umar

Ahmed

et al. 2014

17th IEEE International Multi Topic Conference 2014

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Machine simulation of human reading has been a subject of intensive research for almost four decades. Automatic Urdu character recognition remains a challenging task due to its cursive nature despite the fact that the latest improvements in recognition methods and systems for Latin script are very promising. This work introduces a robust approach based on statistical models that provide solution for recognition of Urdu text Nasta'liq style. Contrary to classical approaches which segment text into words, ligatures or characters, we intend to employ an implicit segmentation where text lines are recognized during segmentation. The developed system will be evaluated on standard Urdu text databases and compared with the state-ofthe-art recognition techniques proposed till date.I.

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Ligature based optical character recognition of Urdu- Nastaleeq font

Cited by 20 publications

References 0 publications

Arabic script based character segmentation: A review

Arabic script based character segmentation: A review

Offline Recognition of Handwritten Urdu Characters using B Spline Curves: A Survey

An Ocr system for printed Nasta'liq script: A segmentation based approach

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