# TFS Recognition: Investigating MPH]{Thai Finger Spelling Recognition: Investigating MediaPipe Hands Potentials

arXiv: https://arxiv.org/abs/2201.03170 · HTML: https://mlsanity.com/paper/2201.03170/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 10 Jan 2022.

## Builds on (most cited first, 8 of 8)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Convolutional Pose Machines](https://mlsanity.com/paper/1602.00134/) (2016)
- [InterHand2.6M: A Dataset and Baseline for 3D Interacting Hand Pose Estimation from a Single RGB Image](https://mlsanity.com/paper/2008.09309/) (2020)
- [Hand Keypoint Detection in Single Images using Multiview Bootstrapping](https://mlsanity.com/paper/1704.07809/) (2017)
- [V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map](https://mlsanity.com/paper/1711.07399/) (2017)
- [Automatic Hand Sign Recognition: Identify Unusuality through Latent Cognizance](https://mlsanity.com/paper/2110.15542/) (2021)

## Abstract (arXiv)

Thai Finger Spelling (TFS) sign recognition could benefit a community of hearing-difficulty people in bridging to a major hearing population. With a relatively large number of alphabets, TFS employs multiple signing schemes. Two schemes of more common signing -- static and dynamic single-hand signing, widely used in other sign languages -- have been addressed in several previous works. To complete the TFS sign recognition, the remaining two of quite distinct signing schemes -- static and dynamic point-on-hand signing -- need to be sufficiently addressed. With the advent of many off-the-shelf hand skeleton prediction models and that training a model to recognize a sign language from scratch is expensive, we explore an approach building upon recently launched MediaPipe Hands (MPH). MPH is a high-precision well-trained model for hand-keypoint detection. We have investigated MPH on three TFS schemes: static-single-hand (S1), simplified dynamic-single-hand (S2) and static-point-on-hand (P1) schemes. Our results show that MPH can satisfactorily address single-hand schemes with accuracy of 84.57% on both S1 and S2. However, our finding reveals a shortcoming of MPH in addressing a point-on-hand scheme, whose accuracy is 23.66% on P1 conferring to 69.19% obtained from conventional classification trained from scratch. This shortcoming has been investigated and attributed to self occlusion and handedness.