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

Document Type : Research Paper

Authors

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

10.22075/ijnaa.2023.31634.4694

Abstract

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 2.99%, a word error rate of 6.67%, and a sentence error rate of 36.87%. 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.

Keywords

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Articles in Press, Corrected Proof
Available Online from 03 November 2023
  • Receive Date: 04 August 2023
  • Revise Date: 31 August 2023
  • Accept Date: 20 September 2023