KPTI: Katib's Pashto Text Imagebase and Deep Learning Benchmark

Ahmad, Riaz; Afzal, Muhammad Zeshan; Rashid, Sheikh Faisal; Liwicki, Marcus; Breuel, Thomas M.; Dengel, Andreas

doi:10.1109/icfhr.2016.0090

Cited by 26 publications

(30 citation statements)

References 18 publications

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“…The KPTI consists of 17, 015 images of Pashto text. To the best of our knowledge, this [20] is the best research work reported particularly for Pashto language. Some other works which used deep learning-based methods for cursive script languages can be explored in references [16]- [19].…”

Section: Related Workmentioning

confidence: 89%

“…A database for Pashto ligatures is also reported in [20]. Authors of the paper used Recurrent Neural Networks to develop a Pashto OCR.…”

Section: Related Workmentioning

confidence: 99%

“…Authors of the paper used Recurrent Neural Networks to develop a Pashto OCR. Tests are performed on a limited set of images in [20]. Authors named their introduced database KPTI.…”

Section: Related Workmentioning

confidence: 99%

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Handwritten Pashto Characters Dataset for Optical Character Recognition

mudaser¹,

Chan²

2021

Preprint

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<div>This work was presenting at the 9th Joint Symposium on Computational Intelligence (JSCI9), organized by the IEEE-CIS Thailand Chapter, that aims to support research students and young researchers, to create a place enabling participants to share and discuss on their research prior to publishing their works. The event was open to all researchers who want to broaden their knowledge in the field of computational intelligence.</div>

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Section: Related Workmentioning

confidence: 89%

“…A database for Pashto ligatures is also reported in [20]. Authors of the paper used Recurrent Neural Networks to develop a Pashto OCR.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Handwritten Pashto Characters Dataset for Optical Character Recognition

mudaser¹,

Chan²

2021

Preprint

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“…Recently introduced Convolutional Neural Networks (CNNs) showed excellent recognition performance in different visual recognition tasks, including OCR systems [24]. A method proposed in [24] is for Pashtu text particularly. The authors of the paper introduced the first text image database for handwritten ligatures called KPTI.…”

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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“…Few studies have also been reported for the Pashto script. An implicit segmentation‐based recognition system was proposed by Ahmad et al, 2016. The authors also presented a dataset for the Pashto language called the KPTI (Katib's Pashto Text Imagebase).…”

Section: Background and Existing Workmentioning

confidence: 99%

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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KPTI: Katib's Pashto Text Imagebase and Deep Learning Benchmark

Cited by 26 publications

References 18 publications

Handwritten Pashto Characters Dataset for Optical Character Recognition

Handwritten Pashto Characters Dataset for Optical Character Recognition

PHND: Pashtu Handwritten Numerals Database and deep learning benchmark

Deep OCR for Arabic script‐based language like Pastho

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