Recognition of inscribed cursive Pashtu numeral through optimized deep learning

Syed S. Khan K. Khan M. Khan R.U. Aloraini A.
2024 PeerJ Inc.

PeerJ Computer Science
2024 #10

Pashtu is one of the most widely spoken languages in south-east Asia. Pashtu Numerics recognition poses challenges due to its cursive nature. Despite this, employing a machine learning-based optical character recognition (OCR) model can be an effective way to tackle this issue. The main aim of the study is to propose an optimized machine learning model which can efficiently identify Pashtu numerics from 0-9. The methodology includes data organizing into different directories each representing labels. After that, the data is preprocessed i.e., images are resized to 32 × 32 images, then they are normalized by dividing their pixel value by 255, and the data is reshaped for model input. The dataset was split in the ratio of 80:20. After this, optimized hyperparameters were selected for LSTM and CNN models with the help of trial-and-error technique. Models were evaluated by accuracy and loss graphs, classification report, and confusion matrix. The results indicate that the proposed LSTM model slightly outperforms the proposed CNN model with a macro-average of precision: 0.9877, recall: 0.9876, F1 score: 0.9876. Both models demonstrate remarkable performance in accurately recognizing Pashtu numerics, achieving an accuracy level of nearly 98%. Notably, the LSTM model exhibits a marginal advantage over the CNN model in this regard.

Convolution neural networks , Long short term memory , Optical character recognition , Pashtu script , Pattern recognition

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Department of IT & CS, Pak-Austria Fachhochschule Institute of Applied Sciences and Technology, KP, Haripur, Pakistan
Department of Computer Science, School of Engineering and Digital Sciences, Nazarbayev University, Astana, Kazakhstan
PAF-IAST, Sino-Pak Center for Artificial Intelligence (SPCAI), Haripur, Pakistan
Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia

Department of IT & CS
Department of Computer Science
PAF-IAST
Department of Information Technology

10 лет помогаем публиковать статьи Международный издатель

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