# Motion-Based Handwriting Recognition and Word Reconstruction

arXiv: https://arxiv.org/abs/2101.06025 · HTML: https://mlsanity.com/paper/2101.06025/

## 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 (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [State-of-the-art Speech Recognition With Sequence-to-Sequence Models](https://mlsanity.com/paper/1712.01769/) (2017)
- [Streaming automatic speech recognition with the transformer model](https://mlsanity.com/paper/2001.02674/) (2020)
- [End-to-End Multi-speaker Speech Recognition with Transformer](https://mlsanity.com/paper/2002.03921/) (2020)
- [A Scalable Handwritten Text Recognition System](https://mlsanity.com/paper/1904.09150/) (2019)
- [End-to-End Automatic Speech Recognition Integrated With CTC-Based Voice Activity Detection](https://mlsanity.com/paper/2002.00551/) (2020)
- [Fully Convolutional Networks for Handwriting Recognition](https://mlsanity.com/paper/1907.04888/) (2019)
- [Handwritten Text Segmentation via End-to-End Learning of Convolutional Neural Network](https://mlsanity.com/paper/1906.05229/) (2019)
- [A Novel Approach for Detection and Ranking of Trendy and Emerging Cyber Threat Events in Twitter Streams](https://mlsanity.com/paper/1907.07768/) (2019)
- [Kinect Sensor Based Gesture Recognition for Surveillance Application](https://mlsanity.com/paper/1812.09595/) (2018)

## 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.