Shape analysis of Pashto script and creation of image database for OCR

Wahab, Mehreen; Amin, Hassan; Ahmed, Farooq

doi:10.1109/icet.2009.5353160

Cited by 19 publications

(17 citation statements)

References 7 publications

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“…The recognition rate of proposed models for Pashto script recognition is directly compared with the state‐of‐the‐art Pashto ligature recognition systems on the FAST‐NU dataset (as given in Table 3). The proposed Pashto ligature recognition system using DenseNet gives a significant improvement in the classification and recognition rate as compared to the systems of Wahab et al (2009), Ahmad, Afzal, Rashid, Liwicki, and Breuel (2015), Ahmad et al (2010), and Ahmad, Naz, Afzal, Amin, and Breuel (2015). Wahab et al (2009) have employed PCA for Pashto ligature recognition.…”

Section: Methodsmentioning

confidence: 87%

“…The proposed Pashto ligature recognition system using DenseNet gives a significant improvement in the classification and recognition rate as compared to the systems of Wahab et al (2009), Ahmad, Afzal, Rashid, Liwicki, and Breuel (2015), Ahmad et al (2010), and Ahmad, Naz, Afzal, Amin, and Breuel (2015). Wahab et al (2009) have employed PCA for Pashto ligature recognition. Ahmed et al used global features (SIFT) for Pashto ligature recognition (Ahmad et al, 2010; Ahmad, Naz, Afzal, Amin, & Breuel, 2015).…”

Section: Methodsmentioning

confidence: 87%

“…The accuracy rates for different studies are compared with the accuracy achieved by the proposed Pashto ligature recognition system model of DenseNet on the FAST‐NU dataset. The study of Wahab et al (2009) could not be compared graphically, because, the accuracy results produced were not reported and satisfactory.…”

Section: Methodsmentioning

confidence: 99%

“…The third experimentation, fixed pages dataset reported accuracy of 96.9%. Likewise, Wahab et al (Wahab et al, 2009) also developed a recognition system for Pashto script using the Principal Component Analysis (PCA) matrix manipulation. The authors also presented a Pashto ligature dataset named “FAST‐NU dataset,” where FAST‐NU is the name of the University where the dataset was formed.…”

Section: Background and Existing Workmentioning

confidence: 99%

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Deep OCR for Arabic script‐based language like Pastho

et al. 2020

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Developing cursive script recognition systems have always been a challenging task for researchers. This article proposes a ligature-based recognition system for the cursive Pashto script using four pre-trained CNN models using a fine-tuned approach. The SqueezeNet, ResNet, MobileNet and DenseNet models have been observed for the classification and the recognition of Pashto sub-word (ligature). Overall, the proposed system is divided into two domains (Source and Target). The source domain contains the pre-trained models used on the ImageNet Dataset. These models are later fine-tuned using the transfer learning approach to be used for the Pashto ligature recognition. The data augmentation techniques of negative and contour are used to increase the representation of ligature images and the dataset size. The CNN models have been evaluated on the benchmarks Pashto ligatures FAST-NU dataset. The proposed system achieved the highest recognition rate of up to 99.31% using the DenseNet architecture of Convolutional Neural Network for Pashto ligature.

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Section: Methodsmentioning

confidence: 87%

Section: Methodsmentioning

confidence: 87%

Section: Methodsmentioning

confidence: 99%

Section: Background and Existing Workmentioning

confidence: 99%

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Deep OCR for Arabic script‐based language like Pastho

et al. 2020

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“…The proposed framework was based on a scale-invariant feature transform along with segmentation. As mentioned earlier, there is no significant research work for POCR systems; some early research work addressing POCR is listed in the references [19][20][21][22][23].…”

Section: Related Workmentioning

confidence: 99%

PHND: Pashtu Handwritten Numerals Database and deep learning benchmark

et al. 2020

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In this paper we introduce a real Pashtu handwritten numerals dataset (PHND) having 50,000 scanned images and make publicly available for research and scientific use. Although more than fifty million people in the world use this language for written and oral communication, no significant efforts are devoted to the Pashtu Optical Character Recognition (POCR). We present a new approach for Pahstu handwritten numerals recognition (PHNR) based on deep neural networks. We train Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) on high-frequency numerals for feature extraction and classification. We evaluated the performance of the proposed algorithm on the newly introduced Pashtu handwritten numerals database PHND and Bangla language number database CMATERDB 3.1.1. We obtained best recognition rate of 98.00% and 98.64% on PHND and CMATERDB 3.1.1. respectively.

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