Primitive Contrastive Learning for Handwritten Mathematical Expression Recognition

Hong-Yu Guo, Chuang Wang, Fei Yin, Heng-Ye Liu, Jin-Wen Wu, Cheng-Lin Liu

Abstract: Contrastive learning has gained significant attention recently as it can learn a representation from a large amount of unlabeled training data to improve downstream tasks. While the existing approaches mainly focus on standard tasks of image classification and object detection, they are not easily applied to structured prediction problems. In this paper, we propose an unsupervised pre-trained model, called PrimCLR, for handwritten mathematical expression recognition. For a formula recognition model of encoder-decoder architecture, a pre-trained representation is obtained by PrimCLR, where the contrastive loss is computed from pairs of patches so as to better discriminate primitives. The pre-trained representation is transferred to downstream formula recognition with supervised fine-tuning. Experiments show that pre-training by PrimCLR can significantly improve the formula recognition performance, and PrimCLR shows superiority to conventional contrastive learning methods. Our model achieves state-of-the-art performance on standard datasets CROHME 2016 and CROHME 2019.