HandReader: Advanced Techniques for Efficient Fingerspelling Recognition

TL;DR

HandReader introduces three innovative architectures utilizing RGB and keypoint data with novel modules, achieving state-of-the-art accuracy in fingerspelling recognition across multiple datasets, including a new Russian dataset.

Contribution

The paper presents three new architectures for fingerspelling recognition, including novel modules TSAM and TPE, and demonstrates their effectiveness on multiple datasets, including a new Russian dataset.

Findings

Achieved state-of-the-art results on ChicagoFSWild datasets.

Demonstrated high performance on the new Znaki Russian fingerspelling dataset.

Proposed novel modules TSAM and TPE for improved temporal and spatial feature processing.

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.