Effective Spelling Correction for Eye-based Typing using domain-specific Information about Error Distribution

Spelling correction methods, widely used and researched, usually assume a low error probability and a small number of errors per word. These assumptions do not hold in very noisy input scenarios such as eye-based typing systems. In particular for eye typing, insertion errors are much more common than in traditional input systems, due to specific sources of noise such as the eye tracker device, particular user behaviors, and intrinsic characteristics of eye movements. The large number of common errors in such a scenario makes the use of traditional approaches unfeasible. Moreover, the lack of a large corpus of errors makes it hard to adopt probabilistic approaches based on information extracted from real world data. We address these problems by combining estimates extracted from general error corpora with domain-specific knowledge about eye-based input. Further, by relaxing restrictions on edit distance specifically related to insertion errors, we propose an algorithm that is able to find dictionary word candidates in an attainable time. We show that our method achieves good results to rank the correct word, given the input stream and similar space and time restrictions, when compared to the state-of-the-art baselines.