Complete Kazakh handwritten page recognition using start, follow and read method

Jantayev R. Kadyrov S. Amirgaliyev Y.
15 July 2021 Little Lion Scientific

Journal of Theoretical and Applied Information Technology
2021 #99 Issue 13 3133 - 3143 pp.

In this article we consider end-to-end full page Handwritten Text Recognition for offline Kazakh text images written in Cyrillic alphabet using Fully connected CNN and bidirectional LSTM. The model performs training of text segmentation and recognition jointly using a new Kazakh text images dataset, named Kazakh Handwritten Dataset (KHD). The novel method, which we introduce, uses three steps: Start, Follow and Read (SFR). The proposed model makes use of Region Proposal Network in order to find the starting coordinates of lines in the page. For the case when lines are not straight, we introduce a method that pursues text lines until the end of it and prepare it for the last recognition step. The SFR model works for Russian language as well since Russian alphabet is a subset of Kazakh alphabet. The experimental analysis shows that on average the model provides 0.11 Character Error Rate.

Bidirectional LSTM , CNN , Computer Vision , Document Processing , HTR , Kazakh Handwritten , Text line cutting , Text Line Follower

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Computer Sciences Department, Suleyman Demirel University, Kazakhstan
Mathematics and Natural Sciences Department, Suleyman Demirel University, Kazakhstan
Al-Farabi Kazakh National University, Kazakhstan

Computer Sciences Department
Mathematics and Natural Sciences Department
Al-Farabi Kazakh National University

10 лет помогаем публиковать статьи Международный издатель

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