Billet Number Recognition Based on Test-Time Adaptation

TL;DR

This paper presents a real-time billet number recognition method that employs test-time adaptation and prior knowledge to improve accuracy amidst image distortions and data distribution shifts.

Contribution

It introduces a novel test-time adaptation approach integrated with prior knowledge for improved billet number recognition accuracy.

Findings

Significant accuracy improvements on real datasets

Effective handling of damaged characters

Robust recognition of both machine-printed and handwritten numbers

Abstract

During the steel billet production process, it is essential to recognize machine-printed or manually written billet numbers on moving billets in real-time. To address the issue of low recognition accuracy for existing scene text recognition methods, caused by factors such as image distortions and distribution differences between training and test data, we propose a billet number recognition method that integrates test-time adaptation with prior knowledge. First, we introduce a test-time adaptation method into a model that uses the DB network for text detection and the SVTR network for text recognition. By minimizing the model's entropy during the testing phase, the model can adapt to the distribution of test data without the need for supervised fine-tuning. Second, we leverage the billet number encoding rules as prior knowledge to assess the validity of each recognition result. Invalid results, which do not comply with the encoding rules, are replaced. Finally, we introduce a validation mechanism into the CTC algorithm using prior knowledge to address its limitations in recognizing damaged characters. Experimental results on real datasets, including both machine-printed billet numbers and handwritten billet numbers, show significant improvements in evaluation metrics, validating the effectiveness of the proposed method.