Class Incremental Learning for Character String Recognition

Yijie Hu, Yan Ming Zhang, Kaizhu Huang, Qiu Feng Wang*

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

Abstract

Character string recognition (CSR) has drawn much attention for document intelligence, but its performance is limited by the pre-defined character set without the ability to recognize new characters. To overcome this issue, class incremental learning (CIL) can be adopted where the model learns from new data instances incrementally over time. However, it is challenging to directly apply existing CIL methods in CSR because CSR is a typical sequence recognition problem. Without accurate alignment, the recognition error of new characters will affect the recognition of other characters in the same sequence. Moreover, the new characters are usually much fewer than the old ones, resulting in a data imbalance issue for learning new classes. To tackle the misalignment issue, we decouple the learning of feature alignment and classifiers during the incremental process in CSR. To handle the data imbalance issue, we propose a Prototype Incremental Learning framework for CSR, namely PIL-CSR. In the PIL-CSR framework, we propose a prototype-centered loss (PCL) to aid the model in facilitating better class separation, and we further propose a prototype separation and feature alignment (PSFA) strategy, allowing the model to adapt and learn new classes seamlessly. Finally, we collect a CSR dataset to evaluate CIL performance (github.com/tambourine666/Doc-CIL). Experimental results demonstrate the effectiveness of our proposed sequence CIL method, obtaining a significant improvement in both line-level and character-level accuracy.

Original languageEnglish
Title of host publicationDocument Analysis and Recognition - ICDAR 2024 - 18th International Conference, Proceedings
EditorsElisa H. Barney Smith, Marcus Liwicki, Liangrui Peng
PublisherSpringer Science and Business Media Deutschland GmbH
Pages405-420
Number of pages16
ISBN (Print)9783031705489
DOIs
Publication statusPublished - 2024
Event18th International Conference on Document Analysis and Recognition, ICDAR 2024 - Athens, Greece
Duration: 30 Aug 20244 Sept 2024

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14808 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Conference on Document Analysis and Recognition, ICDAR 2024
Country/TerritoryGreece
CityAthens
Period30/08/244/09/24

Keywords

  • Character String Recognition
  • Class Incremental Learning
  • OCR
  • Sequence-to-Sequence

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