A new gated multi-scale convolutional neural network architecture for recognition of Persian handwritten texts

سال انتشار: 1403
محل انتشار: مجله آنالیز غیر خطی و کاربردها، دوره: 15، شماره: 10
کد COI اختصاصی: JR_IJNAA-15-10_012
زبان مقاله: انگلیسی
تعداد مشاهده: 196

نویسندگان

Department of Computer Engineering and Information Technology, Razi University, Kermanshah, Iran

چکیده

Due to the ease of writing by hand and the inherent interest in it, writing by hand is still popular among many people. Considering the digitization of today's world and the massive amount of current information on paper, there is a need for a system to convert handwriting into its digital form to speed up access to information and reduce storage space. According to the research carried out in this field, recognizing Persian handwritten texts remains a relatively difficult issue due to the complex and irregular nature of writing and the diversity of people's handwriting. This research introduces a novel method to recognize handwritten texts at the sentence level. To use word recognition methods in sentence recognition, segmentation techniques are needed to separate the words in the sentence. The segmentation algorithm in handwritten texts is inefficient due to overlapping words. Since Recurrent Neural Networks (RNN) were a turning point in the recognition of correct writing, in this article, by removing the segmentation step, a new architecture, an RNN combined with a Gated Multi-scale Convolutional Neural Network (GMCNN), is introduced in order to recognize handwritten sentences. Using the proposed architecture, recognizing Persian handwritten sentences in the Sadri dataset has a character error rate of ۲.۹۹%, a word error rate of ۶.۶۷%, and a sentence error rate of ۳۶.۸۷%. For further evaluation, the proposed method was also evaluated on IAM and Washington datasets. The results show that the proposed method outperforms other known algorithms.

کلیدواژه ها

Handwritten text recognition, Convolutional Neural Network, Recurrent Neural Network, Connectionist temporal classifier

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