@inproceedings{korotaev-etal-2026-handreader,
title = "{H}and{R}eader: Advanced Techniques for Efficient Fingerspelling Recognition",
author = "Korotaev, Pavel and
Surovtsev, Petr and
Kapitanov, Alexander and
Kvanchiani, Karina and
Nagaev, Alexander",
editor = "Singh, Sanjeet and
Joshi, Abhinav and
Dattatreya, Pokala and
Artiaga, Keren and
Pujara, Khushi Jaydeep and
{\v{Z}}elezn{\'y}, Tom{\'a}{\v{s}} and
Gruber, Ivan and
Honz{\'i}k, Jakub and
Kamila, Sabyasachi and
Quiroga, Facundo Manuel and
Hr{\'u}z, Marek and
Hasanuzzaman, Mohammed and
Modi, Ashutosh",
booktitle = "Proceedings of the Second Workshop on Sign Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.wslp-1.19/",
pages = "193--202",
ISBN = "979-8-89176-488-0",
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) operating on keypoints as tensors. Such keypoint composition in a batch allows the encoder to pass them through 2D and 3D convolution layers, utilizing temporal and spatial information and accumulating keypoint 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: on the ChicagoFSWild and ChicagoFSWild+ datasets, HandReader models achieve the best results among methods trained on a single training set, including a new state-of-the-art result on ChicagoFSWild+."
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%0 Conference Proceedings
%T HandReader: Advanced Techniques for Efficient Fingerspelling Recognition
%A Korotaev, Pavel
%A Surovtsev, Petr
%A Kapitanov, Alexander
%A Kvanchiani, Karina
%A Nagaev, Alexander
%Y Singh, Sanjeet
%Y Joshi, Abhinav
%Y Dattatreya, Pokala
%Y Artiaga, Keren
%Y Pujara, Khushi Jaydeep
%Y Železný, Tomáš
%Y Gruber, Ivan
%Y Honzík, Jakub
%Y Kamila, Sabyasachi
%Y Quiroga, Facundo Manuel
%Y Hrúz, Marek
%Y Hasanuzzaman, Mohammed
%Y Modi, Ashutosh
%S Proceedings of the Second Workshop on Sign Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-488-0
%F korotaev-etal-2026-handreader
%X 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) operating on keypoints as tensors. Such keypoint composition in a batch allows the encoder to pass them through 2D and 3D convolution layers, utilizing temporal and spatial information and accumulating keypoint 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: on the ChicagoFSWild and ChicagoFSWild+ datasets, HandReader models achieve the best results among methods trained on a single training set, including a new state-of-the-art result on ChicagoFSWild+.
%U https://aclanthology.org/2026.wslp-1.19/
%P 193-202
Markdown (Informal)
[HandReader: Advanced Techniques for Efficient Fingerspelling Recognition](https://aclanthology.org/2026.wslp-1.19/) (Korotaev et al., WSLP 2026)
ACL