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Found 5 papers, 4 papers with code
TAMER: Tree-Aware Transformer for Handwritten Mathematical Expression Recognition
TAMER combines the advantages of both sequence decoding and tree decoding models by jointly optimizing sequence prediction and tree structure prediction tasks, which enhances the model's understanding and generalization of complex mathematical expression structures.
ICAL: Implicit Character-Aided Learning for Enhanced Handwritten Mathematical Expression Recognition
Significant progress has been made in the field of handwritten mathematical expression recognition, while existing encoder-decoder methods are usually difficult to model global information in $LaTeX$.
Robust image segmentation model based on binary level set
Additionally, to enhance the model's robustness to noisy images, we incorporate the binary level set model into the proposed model.
CoMER: Modeling Coverage for Transformer-based Handwritten Mathematical Expression Recognition
In this paper, we propose CoMER, a model that adopts the coverage information in the transformer decoder.
Handwritten Mathematical Expression Recognition with Bidirectionally Trained Transformer
Encoder-decoder models have made great progress on handwritten mathematical expression recognition recently.
