# Handwritten Mathmatical Expression Recognition

12 papers with code • 4 benchmarks • 5 datasets

Offline Handwritten mathematical Expression Recognition aims to convert 2D images into a 1D structured sequences(LaTeX or MathML)

## Libraries

Use these libraries to find Handwritten Mathmatical Expression Recognition models and implementations## Most implemented papers

# Multi-Scale Dense Networks for Resource Efficient Image Classification

In this paper we investigate image classification with computational resource limits at test time.

# When Counting Meets HMER: Counting-Aware Network for Handwritten Mathematical Expression Recognition

Recently, most handwritten mathematical expression recognition (HMER) methods adopt the encoder-decoder networks, which directly predict the markup sequences from formula images with the attention mechanism.

# Multi-Scale Attention with Dense Encoder for Handwritten Mathematical Expression Recognition

Handwritten mathematical expression recognition is a challenging problem due to the complicated two-dimensional structures, ambiguous handwriting input and variant scales of handwritten math symbols.

# Handwritten Mathematical Expression Recognition with Bidirectionally Trained Transformer

Encoder-decoder models have made great progress on handwritten mathematical expression recognition recently.

# Syntax-Aware Network for Handwritten Mathematical Expression Recognition

In this paper, we propose a simple and efficient method for HMER, which is the first to incorporate syntax information into an encoder-decoder network.

# 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.

# Watch, attend and parse: An end-to-end neural network based approach to handwritten mathematical expression recognition

We employ a convolutional neural network encoder that takes HME images as input as the watcher and employ a recurrent neural network decoder equipped with an attention mechanism as the parser to generate LaTeX sequences.

# Handwritten Mathematical Expression Recognition via Attention Aggregation based Bi-directional Mutual Learning

Handwritten mathematical expression recognition aims to automatically generate LaTeX sequences from given images.

# 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 $LaTeX$.

# PosFormer: Recognizing Complex Handwritten Mathematical Expression with Position Forest Transformer

To overcome this challenge, we propose a position forest transformer (PosFormer) for HMER, which jointly optimizes two tasks: expression recognition and position recognition, to explicitly enable position-aware symbol feature representation learning.