Open AccessA compact deep learning model for Khmer handwritten text recognition
Author(s) -
Bayram Annanurov,
Norliza Mohd Noor
Publication year - 2021
Publication title -
iaes international journal of artificial intelligence
Language(s) - English
Resource type - Journals
eISSN - 2252-8938
pISSN - 2089-4872
DOI - 10.11591/ijai.v10.i3.pp584-591
Subject(s) - computer science , deep learning , digitization , artificial intelligence , task (project management) , layer (electronics) , state (computer science) , convolutional neural network , pattern recognition (psychology) , computer vision , programming language , chemistry , management , organic chemistry , economics
The motivation of this study is to develop a compact offline recognition model for Khmer handwritten text that would be successfully applied under limited access to high-performance computational hardware. Such a task aims to ease the ad-hoc digitization of vast handwritten archives in many spheres. Data collected for previous experiments were used in this work. The oneagainst-all classification was completed with state-of-the-art techniques. A compact deep learning model (2+1CNN), with two convolutional layers and one fully connected layer, was proposed. The recognition rate came out to be within 93-98%. The compact model is performed on par with the state-of-theart models. It was discovered that computational capacity requirements usually associated with deep learning can be alleviated, therefore allowing applications under limited computational power.