论文标题
TFS识别：调查MPH] {泰式手指拼写识别：调查Mediapipe Hands势
TFS Recognition: Investigating MPH]{Thai Finger Spelling Recognition: Investigating MediaPipe Hands Potentials
论文作者
论文摘要
泰国手指拼写（TFS）标志识别可以使障碍人群与主要听力人群的桥梁社区受益。 TFS具有相对较大的字母，采用多个签名方案。在以前的几项工作中已经解决了两种更常见的签名方案 - 静态和动态的单手签名，广泛用于其他符号语言。为了完成TFS符号识别，需要充分解决其余两个完全不同的签名方案 - 静态和动态的点签名。 随着许多现成的手骨架预测模型的出现，并且训练模型从头开始识别手语的识别是昂贵的，我们探索了在最近推出的MediaPipe Hands（MPH）上建立的方法。 MPH是用于手工射击检测的高精度训练良好的模型。 我们已经在三个TFS方案上研究了MPH：静态单个手（S1），简化的动态单手（S2）和静态点（P1）方案。 我们的结果表明，MPH可以令人满意地解决S1和S2的精度为84.57％的单手方案。 但是，我们的发现表明，在解决点对手方案时，MPH存在缺点，该方案的准确性为23.66％，赋予从SCRATCH训练的常规分类中获得的P1的准确性为69.19％。这一缺点已被调查，并归因于自我阻塞和惯用性。
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.
