Coenen, Frans 
ORCID: 0000-0003-1026-6649, Hu, Shu-Ying, Wang, Qiu-Feng, Huang, Kaizhu and Wen, Min
(2022)
Retrieval-Based Language Model Adaptation for Handwritten Chinese Text Recognition.
International Journal on Document Analysis and Recognition (IJDAR), 26 (2).
pp. 109-119.
|
Text
ijdar_manu.pdf - Author Accepted Manuscript Download (1MB) | Preview |
Abstract
In handwritten text recognition, compared to human, computers are far short of linguistic context knowledge, especially domain-matched knowledge. In this paper, we present a novel retrieval-based method to obtain an adaptive language model for offline recognition of unconstrained handwritten Chinese texts. The content of handwritten texts to be recognized is varied and usually unknown a priori. Therefore we adopt a two-pass recognition strategy. In the first pass, we utilize a common language model to obtain initial recognition results, which are used to retrieve the related contents from Internet. In the content retrieval, we evaluate different types of semantic representation from BERT output and the traditional TF–IDF representation. Then, we dynamically generate an adaptive language model from these related contents, which will consequently be combined with the common language model and applied in the second-pass recognition. We evaluate the proposed method on two benchmark unconstrained handwriting datasets, namely CASIA-HWDB and ICDAR-2013. Experimental results show that the proposed retrieval-based language model adaptation yields improvements in recognition performance, despite the reduced Internet contents hereby employed.
| Item Type: | Article |
|---|---|
| Divisions: | Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 15 Mar 2023 09:33 |
| Last Modified: | 27 Oct 2023 01:30 |
| DOI: | 10.1007/s10032-022-00419-2 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3169081 |
Dimensions
Dimensions
