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.
Abstract (translated)
在钢坯生产过程中，实时识别移动中的钢坯上的机器打印或手写编号是至关重要的。为了解决现有场景文本识别方法因图像失真和训练数据与测试数据分布差异等因素导致的低识别精度问题，我们提出了一种结合测试时间适应性调整及先验知识的钢坯编号识别方法。 首先，我们将测试时间适应性调整的方法引入到一个使用DB网络进行文本检测和SVTR网络进行文本识别的模型中。通过在测试阶段最小化模型的熵值，使模型能够适应测试数据的分布特征而无需监督式微调。 其次，我们利用钢坯编号编码规则作为先验知识来评估每个识别结果的有效性。对于不符合编码规则的无效结果，我们会用适当的数值进行替换。 最后，我们将基于先验知识的验证机制引入到CTC算法中，以解决其在识别受损字符时存在的局限性问题。 通过实际数据集（包括机器打印和手写钢坯编号）上的实验结果显示，在评估指标方面取得了显著提升，这证明了我们提出的方法的有效性。
URL
https://arxiv.org/abs/2502.09026
