Holistic Persian Handwritten Word Recognition Using Convolutional Neural Network
Publish Year: 1400
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_IJE-34-8_024
تاریخ نمایه سازی: 12 مرداد 1400
Abstract:
Due to the cursive-ness and high variability of Persian scripts, the segmentation of handwritten words into sub-words is still a challenging task. These issues could be addressed in a holistic approach by sidestepping segmentation at the character level. In this paper, an end-to-end holistic method based on deep convolutional neural network is proposed to recognize off-line Persian handwritten words. The proposed model uses only five convolutional layers and two fully connected layers for classifying word images effectively, which can lead to a substantial reduction in parameters. The effect of various pooling strategies is also investigated in this paper. The primary goal of this article is to ignore handcrafted feature extraction and attain a generalized and stable word recognition system. The presented model is assessed using two famous handwritten Persian word databases called Sadri and IRANSHAHR. The recognition accuracies were obtained at ۹۸.۶% and ۹۴.۶%, on Sadri and IRANSHAHR datasets respectively, and outperformed the state-of-the-art methods.
Keywords:
Persian handwritten word recognition , convolutional neural network , End-to-end learning method , Transfer learning , Persian handwritten dataset
Authors
A. Zohrevand
Computer Engineering Department, Kosar University of Bojnord, Bojnord, Iran
Z. Imani
Computer Engineering Department, Kosar University of Bojnord, Bojnord, Iran
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