University-HMER-TAMER-A3-RealFT

University-HMER-TAMER-A3-RealFT

This repository contains the TAMER-A3 RealFT checkpoint used in the University HMER project.

Model description

TAMER-A3 RealFT is a fast specialist HMER model adapted for handwritten university-level calculus expressions collected from real classroom-style images.

The model follows a TAMER-based architecture with dual adapter components:

• encoder adapter enabled;
• decoder adapter enabled;
• fine-tuned on the real classroom dataset after the University12K adaptation stage.

Intended role

This model is intended as a fast specialist baseline for the collected real-classroom distribution.

It is useful for:

• domain adaptation analysis;
• speed/accuracy trade-off comparison;
• research comparison against Uni-MuMER LoRA;
• reproducing TAMER-A3 RealFT results.

For unconstrained live demo input, the Uni-MuMER LoRA model is recommended as the primary robust model.

Dataset

Dataset: University-HMER-RealClassroom

Real classroom split:

train:       1103 samples
validation:  259 samples
blind test:  274 samples
total:      1636 images

Checkpoint

Uploaded checkpoint:

epoch=56-val_university_ExpRate=0.5637.ckpt

Expected local path in the original research project:

outputs/real_ft_a3_dual_seed7/checkpoints/epoch=56-val_university_ExpRate=0.5637.ckpt

Results

Validation

Blind test

Limitations

The model is specialized for the collected real classroom distribution. It may be less robust for free-form live inputs with different writing styles, lighting, cropping, or spatial layouts.

A known limitation of the real dataset is reduced diversity in operator-bound placement. Many integral and summation bounds are written diagonally or to the right of the operator, while vertically stacked display-style bounds are underrepresented.

Citation / acknowledgement

This work extends the original TAMER implementation:

• TAMER: https://github.com/qingzhenduyu/TAMER