Chinese/English mixed Character Segmentation as Semantic Segmentation

TL;DR

This paper presents a novel approach to multilingual character segmentation by framing it as a semantic segmentation task using fully convolutional networks, effectively handling Chinese/English mixed text with high accuracy.

Contribution

The work introduces a deep learning-based method for multilingual character segmentation, specifically addressing Chinese/English mixed cases with an FCN architecture, outperforming previous techniques.

Findings

Model generalizes well to real-world samples

Significantly outperforms previous methods

Effective for Chinese/English mixed text segmentation

Abstract

OCR character segmentation for multilingual printed documents is difficult due to the diversity of different linguistic characters. Previous approaches mainly focus on monolingual texts and are not suitable for multilingual-lingual cases. In this work, we particularly tackle the Chinese/English mixed case by reframing it as a semantic segmentation problem. We take advantage of the successful architecture called fully convolutional networks (FCN) in the field of semantic segmentation. Given a wide enough receptive field, FCN can utilize the necessary context around a horizontal position to determinate whether this is a splitting point or not. As a deep neural architecture, FCN can automatically learn useful features from raw text line images. Although trained on synthesized samples with simulated random disturbance, our FCN model generalizes well to real-world samples. The experimental results show that our model significantly outperforms the previous methods.