Microsoft Technology Licensing, LLC

Brian Leung

Michael David Revow

Qi Zhang

Ahmad A Abdulkader

Richard Kane Sailor

Henry Allan Rowley

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.

Collecting and utilizing user correction feedback to improve handwriting recognition

Learning to reorder alternates based on a user's personalized vocabulary may be provided. An alternate list provided to a user for replacing words input by the user via a character recognition application may be reordered based on data previously viewed or input by the user (personal data). The alternate list may contain generic data, for example, words for possible substitution with one or more words input by the user. By using the user's personal data and statistical learning methodologies in conjunction with generic data in the alternate list, the alternate list can be reordered to present a top alternate that more closely reflect the user's vocabulary. Accordingly, the user is presented with a top alternate that is more likely to be used by the user to replace data incorrectly input.

Learning to reorder alternates based on a user'S personalized vocabulary

Input handwritten characters are classified as print or cursive based upon numerical feature values calculated from the shape of an input character. The feature values are applied to inputs of an artificial neural network which outputs a probability of the input character being print or cursive. If a character is classified as print, it is analyzed by a print character recognizer. If a character is classified as cursive, it is analyzed using a cursive character recognizer. The cursive character recognizer compares the input character to multiple prototype characters using a Dynamic Time Warping (DTW) algorithm.

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Brian Leung