Computer text entry may be full of noises – for example, computer keyboard users inevitably make typing mistakes and their typing stream implies all users’ self rectification actions. These may produce a great negative influence on the accessibility and usability of applications. This research develops an original Intelligent Keyboard hybrid framework, which can be used to analyze users’ typing stream, and accordingly correct typing mistakes and predict users typing intention. An extendable Focused Time-Delay Neural Network (FTDNN) n-gram prediction algorithm is developed to learn from the users’ typing history and produce text entry prediction and correction based on historical typing data. The results show that FTDNN is an efficient tool to model typing stream. Also, the computer simulation results demonstrate that the proposed framework performs better than using the conventional keyboard.