Motion-Based Handwriting Recognition and Word Reconstruction
Not on the map. Why?
Abstract (arXiv)
In this project, we leverage a trained single-letter classifier to predict the written word from a continuously written word sequence, by designing a word reconstruction pipeline consisting of a dynamic-programming algorithm and an auto-correction model. We conduct experiments to optimize models in this pipeline, then employ domain adaptation to explore using this pipeline on unseen data distributions.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 27 references that are in the map. First posted on arXiv 15 Jan 2021.
Builds on
11 of 27 references.
References · 11
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2015Domain-Adversarial Training of Neural Networkscited by 3,044
- 2017State-of-the-art Speech Recognition With Sequence-to-Sequence Modelscited by 196
- 2020Streaming automatic speech recognition with the transformer modelcited by 73
- 2020End-to-End Multi-speaker Speech Recognition with Transformercited by 42
- 2019A Scalable Handwritten Text Recognition Systemcited by 25
- 2020End-to-End Automatic Speech Recognition Integrated With CTC-Based Voice Activity Detectioncited by 20
- 2019Fully Convolutional Networks for Handwriting Recognitioncited by 10
- 2019Handwritten Text Segmentation via End-to-End Learning of Convolutional Neural Networkcited by 6
- 2019A Novel Approach for Detection and Ranking of Trendy and Emerging Cyber Threat Events in Twitter Streamscited by 5
- 2018Kinect Sensor Based Gesture Recognition for Surveillance Applicationcited by 2
Built on by
Not cited yet.