A New Large Urdu Database for Off-Line Handwriting Recognition

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

doi:10.1007/978-3-642-04146-4_58

Cited by 45 publications

(24 citation statements)

References 6 publications

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“…The CENPARMI Urdu handwritten database [164] comprises Urdu words, characters, digits and numeral strings. A number of native Urdu speakers from different parts of the world contributed to the data collection process.…”

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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“…An Urdu dataset can be found in [27], which includes isolated digits, numeral strings with/without decimal points, five special symbols, 44 isolated characters, and 57 Urdu words. IAM dataset is very popular Roman script dataset which consists of 1539 pages, 5685 sentences, 13353 lines, 115320 words distributed at the document, line, sentence and word level [28].…”

Section: Dataset Developmentmentioning

confidence: 99%

Automatic Line-Level Script Identification From Handwritten Document Images - A Region-Wise Classification Framework for Indian Subcontinent

Obaidullah

Halder

Santosh

et al. 2018

MJCS

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Script identification is a well-studied problem for automatic processing of document images. Several attempts have been made so far, but it is still far ahead from the complete solution. In this paper, an automatic approach for line-level handwritten script identification (HSI), considering eight official Indic scripts namely: Bangla, Devanagari, Kannada, Malayalam, Oriya, Roman, Telugu, and Urdu is proposed. We consider a 148-dimensional feature vector using: image component fractal dimension, structural and visual appearance, directional stroke, interpolation and Gabor energy based texture features. For classification, we divide the whole script dataset based on different regions of India, to study a region-wise classification performance. Experimentation was carried out using the state-of-the-art classifiers: multilayer perceptron (MLP), support vector machine (SVM), random forest (RF), and fuzzy unordered rule induction algorithm (FURIA). Among all, we found that MLP as the best performer in terms of average accuracy of 98.2%, 99.5%, 99.1%, 99.5%, 99.9%, 98%, 98.9% for eight-script, bi-script, eastern, north, south Indian script groups, scripts with 'matra' vs without 'matra', and dravidian vs. non-dravidian groups respectively.

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“…Sagheer et al [29] have made an attempt on the Urdu isolated handwritten digits in which they used Gradient features after developing the dataset on normalized images of digits. Basu et al [30] presented recognition system for postal address code for Latin, Devanagari, Bangla and Urdu.…”

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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A New Large Urdu Database for Off-Line Handwriting Recognition

Cited by 45 publications

References 6 publications

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

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

Automatic Line-Level Script Identification From Handwritten Document Images - A Region-Wise Classification Framework for Indian Subcontinent

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

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