A Compact Deep Learning Model for Khmer Handwritten Text Recognition
Abstract
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.
Keywords
Character Recognition; Convolutional Neural Networks; Deep Learning; Handwriting Recognition; Multilayer Neural Networks;
DOI: http://doi.org/10.11591/ijai.v10.i3.pp%25p
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.