Fingerspelling — the sequential signing of letters that compose proper names, technical terms, and out-of-vocabulary words — is an integral component of any sign language. Despite Russian Sign Language (RSL) being used by more than 150,000 native signers, no public fingerspelling dataset for RSL exists, in contrast to American, British, German, and several other sign languages. We introduce Znaki the first open RSL fingerspelling dataset: 37,252 video recordings (35.4 hours) of 1,593 unique sequences — proper names sampled from 16 domain categories — recorded by 68 native RSL signers (37 Deaf, 29 hard-of-hearing, and 2 certified interpreters) through a crowdsourcing pipeline with a two-stage signer qualification procedure and expert validation. The dataset covers all 33 Cyrillic handshapes, and its letter-frequency distribution matches the Russian National Corpus. As reference points for future research, we establish two baselines adapted from American Sign Language fingerspelling recognition: a Squeezeformer-based encoder-decoder, reaching a character-level accuracy (1-CER) of 0.932 and a sequence accuracy of 0.596, and Fingerspelling-PoseNet, reaching 0.886 and 0.545, respectively.

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+.