This paper investigates the recognition of the Russian fingerspelling alphabet, also known as the Russian Sign Language (RSL) dactyl. Dactyl is a component of sign languages where distinct hand movements represent individual letters of a written language. This method is used to spell words without specific signs, such as proper nouns or technical terms. An alphabet learning simulator is one meaningful application of isolated dactyl recognition. There is a notable issue of data shortage in isolated dactyl recognition: existing Russian dactyl datasets lack subject heterogeneity, contain insufficient samples, or cover only static signs. We provide Bukva, the first open-source video dataset for RSL dactyl recognition that is free of all these shortcomings. It contains 3,757 videos with more than 100 samples for each RSL alphabet sign, including dynamic ones. We utilized crowdsourcing platforms to increase the subjects’ heterogeneity, resulting in the participation of 155 deaf and hard-of-hearing experts in the dataset creation. We use a TSM (Temporal Shift Module) block to handle static and dynamic signs effectively, achieving 83.6% top-1 accuracy with real-time inference on CPU only. The dataset, demo code, and pre-trained models will be made publicly available.