Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Continual Learning for Table Detection in Document Images

Version 1 : Received: 11 August 2022 / Approved: 16 August 2022 / Online: 16 August 2022 (10:56:59 CEST)

A peer-reviewed article of this Preprint also exists.

Minouei, M.; Hashmi, K.A.; Soheili, M.R.; Afzal, M.Z.; Stricker, D. Continual Learning for Table Detection in Document Images. Appl. Sci. 2022, 12, 8969. Minouei, M.; Hashmi, K.A.; Soheili, M.R.; Afzal, M.Z.; Stricker, D. Continual Learning for Table Detection in Document Images. Appl. Sci. 2022, 12, 8969.

Journal reference: Appl. Sci. 2022, 12, 8969
DOI: 10.3390/app12188969

Abstract

The growing amount of data demands methods that can gradually learn from new samples. However, it is not trivial to continually train a network. Retraining a network with new data usually results in a known phenomenon, called “catastrophic forgetting.” In a nutshell, the performance of the model drops on the previous data by learning from the new instances. This paper explores this issue in the table detection problem. While there are multiple datasets and sophisticated methods for table detection, the utilization of continual learning techniques in this domain was not studied. We employed an effective technique called experience replay and performed extensive experiments on several datasets to investigate the effects of catastrophic forgetting. Results show that our proposed approach mitigates the performance drop by 15 percent. To the best of our knowledge, this is the first time that continual learning techniques are adopted for table detection, and we hope this stands as a baseline for future research.

Keywords

table detection; document layout analysis; continual learning; incremental learning; experience replay

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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