Camera-based text entry using American Sign Language (ASL) fingerspelling has become more feasible due to recent advancements in recognition technology. However, there are numerous situations where camera-based text entry may not be ideal or acceptable. To address this, we present FingerSpeller, a solution that enables camera-free text entry using smart rings. FingerSpeller utilizes accelerometers embedded in five smart rings from TapStrap, a commercially available wearable keyboard, to track finger motion and recognize fingerspelling. A Hidden Markov Model (HMM) based backend with continuous Gaussian modeling facilitates accurate recognition as evaluated in a real-world deployment. In offline isolated word recognition experiments conducted on a 1,164-word dictionary, FingerSpeller achieves an average character accuracy of 91% and word accuracy of 87% across three participants. Furthermore, we demonstrate that the system can be downsized to only two rings while maintaining an accuracy level of approximately 90% compared to the original configuration. This reduction in form factor enhances user comfort and significantly improves the overall usability of the system.

PopSign is a smartphone-based bubble-shooter game that helps hearing parentsof deaf infants learn sign language. To help parents practice their ability to sign,PopSign is integrating sign language recognition as part of its gameplay. Fortraining the recognizer, we introduce the PopSign ASL v1.0 dataset that collectsexamples of 250 isolated American Sign Language (ASL) signs using Pixel 4Asmartphone selfie cameras in a variety of environments. It is the largest publiclyavailable, isolated sign dataset by number of examples and is the first dataset tofocus on one-handed, smartphone signs. We collected over 210,000 examplesat 1944x2592 resolution made by 47 consenting Deaf adult signers for whomAmerican Sign Language is their primary language. We manually reviewed 217,866of these examples, of which 175,023 (approximately 700 per sign) were the signintended for the educational game. 39,304 examples were recognizable as a signbut were not the desired variant or were a different sign. We provide a training setof 31 signers, a validation set of eight signers, and a test set of eight signers. Abaseline LSTM model for the 250-sign vocabulary achieves 82.1% accuracy (81.9%class-weighted F1 score) on the validation set and 84.2% (83.9% class-weightedF1 score) on the test set. Gameplay suggests that accuracy will be sufficient forcreating educational games involving sign language recognition.