Abstract
Handwritten character recognition is increasingly important in a variety of automation fields, for example, authentication of bank signatures, identification of ZIP codes on letter addresses, and forensic evidence. Despite improved object recognition technologies, Pashto’s hand-written character recognition (PHCR) remains largely unsolved due to the presence of many enigmatic hand-written characters, enormously cursive Pashto characters, and lack of research attention. We propose a convolutional neural network (CNN) model for recognition of Pashto hand-written characters for the first time in an unrestricted environment. Firstly, a novel Pashto handwritten character data set, “Poha”, for 44 characters is constructed. For preprocessing, deep fusion image processing techniques and noise reduction for text optimization are applied. A CNN model optimized in the number of convolutional layers and their parameters outperformed common deep models in terms of accuracy. Moreover, a set of benchmark popular CNN models applied to Poha is evaluated and compared with the proposed model. The obtained experimental results show that the proposed model is superior to other models with test accuracy of 99.64 percent for PHCR. The results indicate that our model may be a strong candidate for handwritten character recognition and automated PHCR applications.
Highlights
Handwritten character recognition is considered to be one of the most challenging and appealing research areas in the field of pattern recognition and computer vision
The third contribution is a comparison of the developed model, using the Pashto handwritten character data set (Poha) dataset, to benchmark deep neural network models; results showed that the proposed model achieved high accuracy
We achieved 99.64% accuracy for Pashto handwritten characters as showen in Figure 4, which is a higher degree of accuracy than that of the literature relating to Pashto handwritten characters available to date
Summary
Handwritten character recognition is considered to be one of the most challenging and appealing research areas in the field of pattern recognition and computer vision. Based on a review of the literature, it is apparent that inadequate work exists on the recognition of Pashto handwritten characters compared to other foreign language scripts [6,7,8]. The advantage of a convolutional neural networks (CNN) advantage is its applicability to character recognition, such as OCR and hand-written character recognition (HCR), for nearly any available language, i.e., English, Arabic, Hangul, etc. The research related to Pashto handwritten character recognition (PHCR) lacks application of deep learning models, CNN and other popular deep neural network models. Pashto handwritten character recognition using an optimal CNN model and a large-scale data set is proposed. The third contribution is a comparison of the developed model, using the Poha dataset, to benchmark deep neural network models; results showed that the proposed model achieved high accuracy.
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