Abstract: Various technologies and techniques are disclosed that identify possible incorrect recognition results. Handwritten input is received from a user. A recognition operation is performed on the handwritten input to produce an initial recognition result. A possible incorrect recognition is identified using the self-consistency process that identifies the possible incorrect recognition when the initial recognition result is not consistent with a normal writing style of the user. The self-consistency process performs a comparison of the initial recognition result with at least one sample previously provided by the user. If the comparison reveals that the initial recognition result is not consistent with the at least one sample, then the result is identified as possibly incorrect. A classifier confidence process can be alternatively or additionally used to identify a possible incorrect recognition result.

Abstract: Various technologies and techniques are disclosed for improving handwriting recognition using a neural network by allowing a user to provide samples. A recognition operation is performed on the user's handwritten input, and the user is not satisfied with the recognition result. The user selects an option to train the neural network on one or more characters to improve the recognition results. The user is prompted to specify samples for the certain character, word, or phrase, and the neural network is adjusted for the certain character, word, or phrase. Handwritten input is later received from the user. A recognition operation is performed on the handwritten input using the neural network that was adjusted for the certain character or characters.

Abstract: Various technologies and techniques are disclosed for using user corrections to help improve handwriting recognition operations. The system tracks user corrections to recognition results. The system receives handwritten input from the user and performs a recognition operation to determine a top recognized word. The prior corrections made by the user are analyzed to calculate a ratio of times the user has corrected the top recognized word to a particular other word as opposed to correcting the particular other word to the top recognized word. If the ratio meets or exceeds a required minimum, then at least one secondary source is optionally analyzed to determine if the particular other word is used a certain number of times more frequently than the top recognized word in the secondary source. The system performs a swap of the top recognized word with the particular other word when the required criteria are met.

Abstract: An improved system and method for personalizing recognition of an input method is provided. A trainable handwriting recognizer may be personalized by using ink written by the user and text authored by the user. The system includes a personalization service engine and a framework with interfaces for collecting, storing, and accessing user ink and authored information for training recognizers. The trainers of the system may include a text trainer for augmenting a recognizer's dictionary using text content and a shape trainer for tuning generic recognizer components using ink data supplied by a user. The trainers may load multiple trainer clients, each capable of training one or more specific recognizers. Furthermore, a framework is provided for supporting pluggable trainers. Any trainable recognizer may be dynamically personalized using the harvested information authored by the user and ink written by the user.