Online recognition of single stroke handwritten Urdu characters

Haider, Ihtesham; Khan, Kamran Ullah

doi:10.1109/inmic.2009.5383108

Cited by 15 publications

(19 citation statements)

References 4 publications

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“…Using structural features [20,25,26], a model/standard template is designed for each class of letters that contains all the significant information with which test classes are compared. Statistical approach uses the information of the underlying statistical distribution of some measurable events or phenomena of interest in the input data [22,23]. With global transformation features, the recognition problem is taken up in frequency domain using transformations like Fourier, discrete cosine, Gabor, and Walsh-Hadamard etc.…”

Section: Feature Extractionmentioning

confidence: 99%

“…With structural, statistical and global transformation features as used in [20,25,26,22,23,19] it is not possible to find out these landmarks exactly. In proposed study, wavelet transformation of handwritten stroke data enables us to accurately pinpoint the mentioned landmarks and leads to attain better recognition rates.…”

Section: Wavelet Featuresmentioning

confidence: 99%

“…In this study, for comparison purpose, in addition to wavelet based features, structural features proposed by Khan and Haider [22,23], have also been employed and tested. It is shown in the results (Section 4) that with wavelet features the recognition accuracy is far better than that obtained with structural features.…”

Section: Structural Featuresmentioning

confidence: 99%

“…On the basis of target-set, Urdu handwriting recognition (both off-line and online) can be placed into three categories: isolated or full form character recognition [20,21,22,23], selecting ligatures for recognition or holistic approach (also known as segmentation-free approach) [17,24,25,26,27,28], and segmentation-based or analytical approach [29,30,31,32,33,34,35].…”

mentioning

confidence: 99%

“…Inspired from [22,23,39] the authors propose in this work the online Urdu character recognition problem for context-dependent shapes of Urdu characters, that is, for half-forms. For the development of online cursive Urdu handwriting recognition system, recognition of half form Urdu characters is a primary step because of the following four reasons: First, Urdu characters appear in half forms in a word.…”

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confidence: 99%

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A Novel Similar Character Discrimination Method for Online Handwritten Urdu Character Recognition in Half Forms

2018

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Online handwritten Urdu character recognition is one of the key technologies for intelligent interface on smart phones and touch screens. It is a challenging research topic as Urdu script has many similar character groups. A novel similar character discrimination method for online handwritten Urdu character recognition is proposed in this paper which includes pre-classification, feature extraction and fine classification process. The pre-classifier enables the discrimination of similar characters by putting them in distinct smaller subsets according to stroke number and diacritics. Then structural features and wavelet features are extracted. Finally, Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Recurrent Neural Network (RNN) classifiers are compared for fine classification within subsets. Results of RNN classifier without using the proposed pre-classifier and features have also been obtained to check the end-to-end capability of the RNN classifier. Experimental results show that the proposed method is efficient and achieves an overall accuracy of 96% on a large-scale self-collected dataset. It is feasible to extend this method for other Arabic scripts.

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Section: Feature Extractionmentioning

confidence: 99%

Section: Wavelet Featuresmentioning

confidence: 99%

Section: Structural Featuresmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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A Novel Similar Character Discrimination Method for Online Handwritten Urdu Character Recognition in Half Forms

2018

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The optical character recognition of Urdu-like cursive scripts

Naz

Hayat

Razzak

et al. 2014

Pattern Recognition

118

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Urdu handwritten text recognition: a survey

et al. 2020

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Work on the problem of handwritten text recognition in Urdu script has been an active research area. A significant progress is made in this interesting and challenging field in the last few years. In this study, the authors presented a comprehensive survey for a number of offline and online handwritten text recognition systems for Urdu script written in Nastaliq font style from 2004 to 2019. Following features make their contribution worthwhile and unique among the reviews of a similar kind: (i) their review classifies the existing studies based on types of recognition systems used for Urdu handwritten text, (ii) it covers a very different outlook of the recognition process of the Urdu handwritten text at different granularity levels (e.g. character, word, ligature, or sentence level), (iii) this review article also presents each of surveyed articles in following dimensions: the task performed, its granularity level, dataset used, results obtained, and future dimensions, and (iv) lastly it gives the summary of the surveyed articles according to the granularity levels, publishing years, related tasks or subtasks, and types of classifiers used. In the end, major challenges and tasks related to Urdu handwritten text recognition approaches are also discussed in detail.

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Online recognition of single stroke handwritten Urdu characters

Cited by 15 publications

References 4 publications

A Novel Similar Character Discrimination Method for Online Handwritten Urdu Character Recognition in Half Forms

A Novel Similar Character Discrimination Method for Online Handwritten Urdu Character Recognition in Half Forms

The optical character recognition of Urdu-like cursive scripts

Urdu handwritten text recognition: a survey

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