Abstract
Along with the innovation and development of society, millions of documents are generated daily, and new types of documents related to new activities and services appear regularly. In the workflow for processing these documents, the first step is to classify the received documents to assign them to the relevant departments or staff. Therefore, two major problems arise: 1) the document classification algorithm is required to not only learn new categories efficiently but also have low error rate on them; 2) document classification needs to have the ability to detect when new classes appear. To address the first problem, we propose a class incremental learning method combined with ambiguity rejection to reduce the error rate of document classification. In this method, the ambiguity rejection module uses the classifier’s probabilities and statistical analysis to optimize per-class thresholds for filtering out (rejecting) uncertain results, thereby increasing the accuracy of non-filtered documents. Our method is thoroughly evaluated on a public business document dataset and on our private administrative document dataset including 36 categories of more than 23 thousand images. On our private dataset, the method increases the classification accuracy of 0.82%, from 98.28% to 99.10%, while the rejection rate is only 3.42%. Additionally, with incremental learning scenario, it can achieve error and rejection rates of 1% and 8.98%, respectively. This result has great significance, can also be applied to similar document datasets, and especially has great potential for real-world applications.
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Acknowledgement
This work was supported by the French government in the framework of the France Relance program and by the YOOZ company under the grant number ANR-21-PRRD-0010-01. We also would like to thank Guénael Manic, Mohamed Saadi, Jonathan Ouellet and Jérôme Lacour from YOOZ for their support.
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Pham, TC., Coustaty, M., Joseph, A., Poulain d’Andecy, V., Visani, M., Sidere, N. (2023). Incremental Learning and Ambiguity Rejection for Document Classification. In: Fink, G.A., Jain, R., Kise, K., Zanibbi, R. (eds) Document Analysis and Recognition - ICDAR 2023. ICDAR 2023. Lecture Notes in Computer Science, vol 14191. Springer, Cham. https://doi.org/10.1007/978-3-031-41734-4_2
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