Recognition of Images of Korean Characters Using Embedded Networks

TL;DR

This paper introduces a lightweight embedded network method for recognizing Korean hieroglyph images, achieving higher accuracy than open-source OCR and suitable for mobile devices.

Contribution

The paper proposes a novel, efficient recognition method for Korean hieroglyphs using embedded networks, tailored for mobile device implementation.

Findings

Recognition accuracy surpasses open-source OCR frameworks.

Method is suitable for deployment on mobile devices.

Training based on data similarity improves recognition performance.

Abstract

Despite the significant success in the field of text recognition, complex and unsolved problems still exist in this field. In recent years, the recognition accuracy of the English language has greatly increased, while the problem of recognition of hieroglyphs has received much less attention. Hieroglyph recognition or image recognition with Korean, Japanese or Chinese characters have differences from the traditional text recognition task. This article discusses the main differences between hieroglyph languages and the Latin alphabet in the context of image recognition. A light-weight method for recognizing images of the hieroglyphs is proposed and tested on a public dataset of Korean hieroglyph images. Despite the existing solutions, the proposed method is suitable for mobile devices. Its recognition accuracy is better than the accuracy of the open-source OCR framework. The presented method of training embedded net bases on the similarities in the recognition data.