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Combining convolutional neural networks with SVM classifier for recognizing Persian and Arabic handwritten words

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Abstract

Convolutional Neural Networks (CNNs) show state-of-the-art performance in handwritten word recognition. Existing CNNs operate on one language and their accuracy depends on dataset. Besides, since CNNs automatically extract features from the raw images, pre-processing is not usually applied. In this paper, we aim to achieve two goals: improving the accuracy of recognition task by pre-processing the raw images and designing a CNN structure for successful operation on similar languages (Persian and Arabic here). For doing this, a new CNN structure named PCNS is proposed as follows: images are firstly pre-processed to have the same pixel size. After that, images are given into GoogLeNet for high and low-level feature extraction. Finally, Support Vector Machine (SVM) makes the final classification. Experiments indicate that PCNS statistically outperforms other methods on Persian language (P value = 0.048) and provides competitive results with others on Arabic datasets. Test accuracies on Persian datasets are: Iranshahr (98.62%), Hoda (99.50%), and Farshid_LATP (98.83%). On Arabic datasets: MADBase (99.20%), HACDB (95.96%), and IFN/ENIT (97.65%).

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  1. Deep Learning Research Group and Department of Electrical and Computer Engineering, University of Hormozgan, Bandar Abbas, Iran

    Shahram Golzari & Abdullah Khalili

  2. Department of Electrical and Computer Engineering, University of Hormozgan, Bandar Abbas, Iran

    Rasool Sabzi

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  1. Shahram Golzari
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  2. Abdullah Khalili
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Golzari, S., Khalili, A. & Sabzi, R. Combining convolutional neural networks with SVM classifier for recognizing Persian and Arabic handwritten words. Multimed Tools Appl 81, 33785–33799 (2022). https://doi.org/10.1007/s11042-022-13101-w

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