During the last two decades signicant work has been reported in the eld of cursive language’s recognition especially,in the Arabic,the Urdu and the Persian languages.The unavailability of such work in the Pashto lang...During the last two decades signicant work has been reported in the eld of cursive language’s recognition especially,in the Arabic,the Urdu and the Persian languages.The unavailability of such work in the Pashto language is because of:the absence of a standard database and of signicant research work that ultimately acts as a big barrier for the research community.The slight change in the Pashto characters’shape is an additional challenge for researchers.This paper presents an efcient OCR system for the handwritten Pashto characters based on multi-class enabled support vector machine using manifold feature extraction techniques.These feature extraction techniques include,tools such as zoning feature extractor,discrete cosine transform,discrete wavelet transform,and Gabor lters and histogram of oriented gradients.A hybrid feature map is developed by combining the manifold feature maps.This research work is performed by developing a medium-sized dataset of handwritten Pashto characters that encapsulate 200 handwritten samples for each 44 characters in the Pashto language.Recognition results are generated for the proposed model based on a manifold and hybrid feature map.An overall accuracy rates of 63.30%,65.13%,68.55%,68.28%,67.02%and 83%are generated based on a zoning technique,HoGs,Gabor lter,DCT,DWT and hybrid feature maps respectively.Applicability of the proposed model is also tested by comparing its results with a convolution neural network model.The convolution neural network-based model generated an accuracy rate of 81.02%smaller than the multi-class support vector machine.The highest accuracy rate of 83%for the multi-class SVM model based on a hybrid feature map reects the applicability of the proposed model.展开更多
The optical character recognition for the right to left and cursive languages such as Arabic is challenging and received little attention from researchers in the past compared to the other Latin languages.Moreover,the...The optical character recognition for the right to left and cursive languages such as Arabic is challenging and received little attention from researchers in the past compared to the other Latin languages.Moreover,the absence of a standard publicly available dataset for several low-resource lan-guages,including the Pashto language remained a hurdle in the advancement of language processing.Realizing that,a clean dataset is the fundamental and core requirement of character recognition,this research begins with dataset generation and aims at a system capable of complete language understanding.Keeping in view the complete and full autonomous recognition of the cursive Pashto script.The first achievement of this research is a clean and standard dataset for the isolated characters of the Pashto script.In this paper,a database of isolated Pashto characters for forty four alphabets using various font styles has been introduced.In order to overcome the font style shortage,the graphical software Inkscape has been used to generate sufficient image data samples for each character.The dataset has been pre-processed and reduced in dimensions to 32×32 pixels,and further converted into the binary format with a black background and white text so that it resembles the Modified National Institute of Standards and Technology(MNIST)database.The benchmark database is publicly available for further research on the standard GitHub and Kaggle database servers both in pixel and Comma Separated Values(CSV)formats.展开更多
基金funded by Qatar University Internal Grant under Grant No.IRCC-2020-009.The ndings achieved herein are solely the responsibility of the authors。
文摘During the last two decades signicant work has been reported in the eld of cursive language’s recognition especially,in the Arabic,the Urdu and the Persian languages.The unavailability of such work in the Pashto language is because of:the absence of a standard database and of signicant research work that ultimately acts as a big barrier for the research community.The slight change in the Pashto characters’shape is an additional challenge for researchers.This paper presents an efcient OCR system for the handwritten Pashto characters based on multi-class enabled support vector machine using manifold feature extraction techniques.These feature extraction techniques include,tools such as zoning feature extractor,discrete cosine transform,discrete wavelet transform,and Gabor lters and histogram of oriented gradients.A hybrid feature map is developed by combining the manifold feature maps.This research work is performed by developing a medium-sized dataset of handwritten Pashto characters that encapsulate 200 handwritten samples for each 44 characters in the Pashto language.Recognition results are generated for the proposed model based on a manifold and hybrid feature map.An overall accuracy rates of 63.30%,65.13%,68.55%,68.28%,67.02%and 83%are generated based on a zoning technique,HoGs,Gabor lter,DCT,DWT and hybrid feature maps respectively.Applicability of the proposed model is also tested by comparing its results with a convolution neural network model.The convolution neural network-based model generated an accuracy rate of 81.02%smaller than the multi-class support vector machine.The highest accuracy rate of 83%for the multi-class SVM model based on a hybrid feature map reects the applicability of the proposed model.
文摘The optical character recognition for the right to left and cursive languages such as Arabic is challenging and received little attention from researchers in the past compared to the other Latin languages.Moreover,the absence of a standard publicly available dataset for several low-resource lan-guages,including the Pashto language remained a hurdle in the advancement of language processing.Realizing that,a clean dataset is the fundamental and core requirement of character recognition,this research begins with dataset generation and aims at a system capable of complete language understanding.Keeping in view the complete and full autonomous recognition of the cursive Pashto script.The first achievement of this research is a clean and standard dataset for the isolated characters of the Pashto script.In this paper,a database of isolated Pashto characters for forty four alphabets using various font styles has been introduced.In order to overcome the font style shortage,the graphical software Inkscape has been used to generate sufficient image data samples for each character.The dataset has been pre-processed and reduced in dimensions to 32×32 pixels,and further converted into the binary format with a black background and white text so that it resembles the Modified National Institute of Standards and Technology(MNIST)database.The benchmark database is publicly available for further research on the standard GitHub and Kaggle database servers both in pixel and Comma Separated Values(CSV)formats.
