Residual BiRNN Based Seq2Seq Model with Transition Probability Matrix for Online Handwritten Mathematical Expression Recognition-Reference-Cited by-同舟云学术

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Residual BiRNN Based Seq2Seq Model with Transition Probability Matrix for Online Handwritten Mathematical Expression Recognition

Published:2019-09 Issue: Volume: Page:
ISSN:
Container-title:2019 International Conference on Document Analysis and Recognition (ICDAR)
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Author:

Hong Zelin,You Ning,Tan Jun,Bi Ning

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/8961318/8977948/08978167.pdf?arnumber=8978167

Cited by 15 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Improving Handwritten Mathematical Expression Recognition via Integrating Convolutional Neural Network With Transformer and Diffusion-Based Data Augmentation;IEEE Access;2024

2. Machine learning and non-machine learning methods in mathematical recognition systems: Two decades’ systematic literature review;Multimedia Tools and Applications;2023-08-23

3. An encoder-decoder based generation model for online handwritten mathematical expressions;Journal of Image and Graphics;2023

4. A dive in white and grey shades of ML and non-ML literature: a multivocal analysis of mathematical expressions;Artificial Intelligence Review;2022-12-06

5. Combining CNN and Transformer as Encoder to Improve End-to-End Handwritten Mathematical Expression Recognition Accuracy;Frontiers in Handwriting Recognition;2022

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