HandReader: Advanced Techniques for Efficient Fingerspelling Recognition

• URL: http://arxiv.org/abs/2505.10267v1
• Date: Thu, 15 May 2025 13:18:37 GMT
• Title: HandReader: Advanced Techniques for Efficient Fingerspelling Recognition
• Authors: Pavel Korotaev, Petr Surovtsev, Alexander Kapitanov, Karina Kvanchiani, Aleksandr Nagaev,
• Abstract summary: This paper introduces HandReader, a group of three architectures designed to address the fingerspelling recognition task.<n>HandReader$_RGB$ employs the novel Adaptive Shift-Temporal Module (TSAM) to process RGB features from videos of varying lengths.<n>HandReader$_KP$ is built on the proposed Temporal Pose (TPE) operated on keypoints as tensors.<n>Each HandReader model possesses distinct advantages and achieves state-of-the-art results on the ChicagoFSWild and ChicagoFSWild+ datasets.
• Score: 75.38606213726906
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Fingerspelling is a significant component of Sign Language (SL), allowing the interpretation of proper names, characterized by fast hand movements during signing. Although previous works on fingerspelling recognition have focused on processing the temporal dimension of videos, there remains room for improving the accuracy of these approaches. This paper introduces HandReader, a group of three architectures designed to address the fingerspelling recognition task. HandReader$_{RGB}$ employs the novel Temporal Shift-Adaptive Module (TSAM) to process RGB features from videos of varying lengths while preserving important sequential information. HandReader$_{KP}$ is built on the proposed Temporal Pose Encoder (TPE) operated on keypoints as tensors. Such keypoints composition in a batch allows the encoder to pass them through 2D and 3D convolution layers, utilizing temporal and spatial information and accumulating keypoints coordinates. We also introduce HandReader_RGB+KP - architecture with a joint encoder to benefit from RGB and keypoint modalities. Each HandReader model possesses distinct advantages and achieves state-of-the-art results on the ChicagoFSWild and ChicagoFSWild+ datasets. Moreover, the models demonstrate high performance on the first open dataset for Russian fingerspelling, Znaki, presented in this paper. The Znaki dataset and HandReader pre-trained models are publicly available.

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