A compact deep learning model for Khmer handwritten text recognition

Bayram Annanurov, Norliza Mohd Noor 

Department of Computer Science, Paragon International University, Cambodia 

Department of Engineering, Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, Malaysia

Abstract (of the Paper)

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.

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