UIT-HWDB: Using Transferring Method to Construct A Novel Benchmark for Evaluating Unconstrained Handwriting Image Recognition in Vietnamese

TL;DR

This paper introduces a novel transferring method to create a high-quality synthetic Vietnamese handwriting dataset, enabling more effective evaluation of recognition methods amid language-specific complexities.

Contribution

It proposes a transferring approach to generate a complex Vietnamese handwriting dataset, addressing resource scarcity and evaluation challenges in offline handwriting recognition.

Findings

The synthetic dataset effectively captures natural handwriting attributes.

State-of-the-art methods face significant challenges on the new dataset.

The dataset facilitates more realistic evaluation of recognition techniques.

Abstract

Recognizing handwriting images is challenging due to the vast variation in writing style across many people and distinct linguistic aspects of writing languages. In Vietnamese, besides the modern Latin characters, there are accent and letter marks together with characters that draw confusion to state-of-the-art handwriting recognition methods. Moreover, as a low-resource language, there are not many datasets for researching handwriting recognition in Vietnamese, which makes handwriting recognition in this language have a barrier for researchers to approach. Recent works evaluated offline handwriting recognition methods in Vietnamese using images from an online handwriting dataset constructed by connecting pen stroke coordinates without further processing. This approach obviously can not measure the ability of recognition methods effectively, as it is trivial and may be lack of features that are essential in offline handwriting images. Therefore, in this paper, we propose the Transferring method to construct a handwriting image dataset that associates crucial natural attributes required for offline handwriting images. Using our method, we provide a first high-quality synthetic dataset which is complex and natural for efficiently evaluating handwriting recognition methods. In addition, we conduct experiments with various state-of-the-art methods to figure out the challenge to reach the solution for handwriting recognition in Vietnamese.