In recent years, tree decoders become more popular than LaTeX string decoders in the field of handwritten mathematical expression recognition (HMER) as they can capture the hierarchical tree structure of mathematical expressions. However previous tree decoders converted the tree structure labels into a fixed and ordered sequence, which could not make full use of the diversified expression of tree labels. In this study, we propose a novel tree decoder (TDv2) to fully utilize the tree structure labels. Compared with previous tree decoders, this new model does not require a fixed priority for different branches of a node during training and inference, which can effectively improve the model generalization capability. The input and output of the model make full use of the tree structure label, so that there is no need to find the parent node in the decoding process, which simplifies the decoding process and adds a prior information to help predict the node. We verified the effectiveness of each part of the model through comprehensive ablation experiments and attention visualization analysis. On the authoritative CROHME 14/16/19 datasets, our method achieves the state-of-the-art results.

TDv2: A Novel Tree-Structured Decoder for Offline Mathematical Expression Recognition


Recently, a novel radical analysis network (RAN) has the
capability of effectively recognizing unseen Chinese
character classes and largely reducing the requirement of
training data by treating a Chinese character as a
hierarchical composition of radicals rather than a single
character class.} However, when dealing with more
challenging issues, such as the recognition of complicated
characters, low-frequency character categories, and
characters in natural scenes, RAN still has a lot of room
for improvement. In this paper, we explore options to
further improve the structure generalization and robustness
capability of RAN with the Transformer architecture, which
has achieved start-of-the-art results for many
sequence-to-sequence tasks. More specifically, we propose
to replace the original attention module in RAN with the
transformer decoder, which is named as a transformer-based
radical analysis network (RTN). The experimental results
show that the proposed approach can significantly
outperform the RAN on both printed Chinese character
database and natural scene Chinese character database.
Meanwhile, further analysis proves that RTN can be better
generalized to complex samples and low-frequency
characters, and has better robustness in recognizing
Chinese characters with different attributes.

A Transformer-based Radical Analysis Attention Network for 
Chinese Character Recognition

Main_Track

technical paper

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

AAAI 2022

PS T4.2: Text Understanding and Character Recognition

poster

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


Chen Yang

2

SHORT BIO

Presentations

TDv2: A Novel Tree-Structured Decoder for Offline Mathematical Expression Recognition

A Transformer-based Radical Analysis Attention Network for Chinese Character Recognition

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES