Optical Character Recognition System for Urdu Words in Nastaliq Font

Shabbir, Safia; Siddiqi, Imran

doi:10.14569/ijacsa.2016.070575

Cited by 16 publications

(14 citation statements)

References 32 publications

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“…We adapt a sequential algorithm [1,34,35] that employs dynamic time warping (DTW) [1] for clustering of ligatures. The algorithm does not require the number of desired classes in advance.…”

Section: Ligature Clusteringmentioning

confidence: 99%

“…OCR is one of the most researched pattern classification problems. Today, commercially mature OCRs are available realizing high recognition rates on a number of scripts, those based on Latin and Chinese alphabets for instance [1,2]. Despite these developments, OCRs for many languages are yet either to be developed or are in very early stages, and cursive Urdu being one of such example is investigated in our study.…”

Section: Introductionmentioning

confidence: 99%

“…The alphabet of Urdu is a super set of Arabic, borrows some characters from Pashto, and comprises a total of 39 *Correspondence: imran.siddiqi@gmail.com 1 Bahria University, Islamabad, Pakistan Full list of author information is available at the end of the article characters. Unlike Pashto and Arabic which are mostly scripted in the Naskh style, Urdu generally employs the Nastaliq script which runs diagonally from right to left.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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“…We adapt a sequential algorithm [1,34,35] that employs dynamic time warping (DTW) [1] for clustering of ligatures. The algorithm does not require the number of desired classes in advance.…”

Section: Ligature Clusteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Segmentation-free optical character recognition for printed Urdu text

Din

Siddiqi

Khalid

et al. 2017

J Image Video Proc.

Self Cite

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“…In the literature, there are already existing works to improve the accuracy of ID card reading by different techniques before the recognition of optical characters [1], [2], [6], [7], [8], [9], [10], [11]. But for the Vietnamese ID Card, especially with the old form, it still lacks an efficient method to improve the quality of input data, reduce noise or time for the recognition task.…”

Section: Introductionmentioning

confidence: 99%

A Method for Segmentation of Vietnamese Identification Card Text Fields

Thanh¹,

Nguyen²

2019

IJACSA

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The development of deep learning in computer vision has motivated researches in related fields, including Optical Character Recognition (OCR). Many proposed models and pre-trained models in the literature demonstrate their efficient in optical text recognition. In this context, image processing techniques has an essential role in improving the accuracy of recognition task. Because, depending on the practical application, image text often suffering several degradation from blur, uneven illumination, complex background, perspective distortion and so on. In this paper, we propose a method for pre-processing, text area extraction and segmentation of Vietnamese Identification Card, in order to improve the accuracy of Region of Interest detection. The proposed method was evaluated with a large data set with different practical qualities. Experiment results demonstrate the efficiency of our method.

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“…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%

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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Optical Character Recognition System for Urdu Words in Nastaliq Font

Cited by 16 publications

References 32 publications

Segmentation-free optical character recognition for printed Urdu text

Segmentation-free optical character recognition for printed Urdu text

A Method for Segmentation of Vietnamese Identification Card Text Fields

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

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