© Authors 2017, All Rights Reserved INTERACT 2017 Adjunct Proceedings Probabilistic Modeling of Swarachakra Keyboard for Improved Touch Accuracy Nikhil Wani 1 , Adarsh Patodi 2 and Sumit Singh Yadav 3 1 Vishwakarma Institute of Technology, Pune, India nikhil.wani14@vit.edu 1 2 ITM University, Gwalior, Madhya Pradesh, India 3 Indian Institute of Technology (IIT), Bombay, Mumbai, India {adarshpatodi 2 , ssysumitsingh 3 }@gmail.com Abstract. We present a probabilistic machine learning approach to reduce touch errors on an Indic script keyboard – Swarachakra. As of now the model is built purely based on the keyboard model, which extends to a probabilistic model, and is functionally independent of the language model. It is learned using 18,240 recorded touch inputs for which it uses a Naive Bayes classifier and assigns an adapted probability distribution to each of the 39 class labels, i.e. the keys on the keyboard. We show that a comparative reduction of error rate by 7.47% against the Non-Probabilistic model and 1.15%-3.15% against the baseline Swarachakra model was obtained when modeled using a probabilistic approach. Looking into the future, a hybrid model with incorporation of a language model will be designed to factor in with the keyboard model which may further meet user specific needs. Keywords: Touchscreen text input, Machine learning, Classification. 1 Introduction In this paper, we propose a data-driven probabilistic approach to touch. We treat the problem at the intersection of a HCI approach and a Machine learning task where we are interested in assigning the correct probability value to the user input touch by mapping it to the intended touch input. Given that the text input in Indic scripts often involve typing a consonant and a vowel in combination and handling of the ‘chakra’, i.e. a circular input that appears on touch [2], is effective in context of the Indic keyboard, makes the first touch of the chakra naturally important. In recent analysis study, [6] reported an initial moderately high error rate of 13-18% on average which stabilizes to 6- 8% as user session keep increasing and also suggest room for improvement in the corrected error rates. We identify that the challenge arises when the user