A compact deep learning model for khmer handwritten text recognition

• Main Authors: Annanurov, B., Noor, N.
• Format: Article
• Language: English
• Published: Institute of Advanced Engineering and Science 2021
• Subjects: T Technology (General)
• Online Access: http://eprints.utm.my/id/eprint/94987/1/NorlizaNoor2021_ACompactDeepLearningModel.pdf; http://eprints.utm.my/id/eprint/94987/; http://dx.doi.org/10.11591/ijai.v10.i3.pp584-591

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 one-against-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-the-art models. It was discovered that computational capacity requirements usually associated with deep learning can be alleviated, therefore allowing applications under limited computational power.