Microsoft Technology Licensing, LLC

Angshuman Guha

Patrick M Haluptzok

Robert L Chambers

Yutaka Suzue

Joshua Clow

Mukundan Bhoovaraghavan

Ranjit Kumar Gupta

Anuradha N Padte

Vamshidhar G Reddy

Qi Zhang

Ahmad A Abdulkader

James A Pittman

Henry Allan Rowley

An improved system and method for providing context information of executable code to an input method is provided. Advanced text input methods may be made aware of the type of text expected to be received as input so that input methods may achieve a higher accuracy in recognition of text input. Generic interfaces provide a framework for supporting application authoring platforms to allow application developers to easily specify context information to the system and have it reliably forwarded to the correct input methods. Additionally, a context tagging tool may associate specific text input fields of an existing application with an input scope without modifying the application itself. The context tagging tool may create a manifest that contains tags associating the specific text input fields with an input scope. Any advanced input methods use by the application may be updated with instructions for accessing the context information stored in the manifest.

System and method for providing context to an input method

A method and apparatus relating to the recognition of free-form data input is disclosed. The method involves ordering writing strokes based upon spatial relationships between the strokes, and partitioning the strokes into segments. The segments are then ordered based upon spatial relationships between the segments, and the segments are analyzed to associate characters with the segments. The apparatus includes a capture interface, an ordering module, and a recognition module. The capture interface records the strokes, and the ordering module orders segments of the strokes based upon spatial relationships of the segments. The recognition module analyzes the segments and associates characters with the segments.

Recognition of electronic ink with late strokes

System and method for providing context to an input method by tagging existing applications

Ink strokes of cursive writing are segmented to make the cursive writing more like print writing, particularly with respect to the number of strokes of a character. A stroke-segmentation module first finds the local extrema points on a stroke of input ink. Then the local extrema points are stepped through, two (or three) at a time. The stroke-segmentation module may compare the three (or four) ink segments that are adjacent to the two (or three) local extrema points to a set of predefined stroke-segmentation patterns to find a closest matching pattern. Strokes are then segmented based on a stroke-segmentation rule that corresponds to the closest matching pattern. Additional stroke segmentation may be performed based on the change of curvature of the segmented ink strokes. Then, a character-recognition module performs character recognition processing by comparing the segmented ink strokes to prototype samples at least some of which have been similarly segmented.

Stroke segmentation for template-based cursive handwriting recognition

A feature extractor samples points on an input pattern drawn in real time. In addition to X and Y coordinates, each sample has an up/down or Z coordinate value representing whether or not the sample point is a perceptible part of the pattern. The groups of coordinate values are transformed into respective sets of coefficients of Chebyshev polynomials by least-squares fit. A recognition unit can use the coefficients as features to identify the character. Additional features, such as aspect ratio and center of gravity, are also derived to aid in recognizing the pattern or reconstructing its image.

Feature extraction for real-time pattern recognition using single curve per pattern analysis

A method and system for recognizing user input information including cursive handwriting and spoken words. A time-delayed neural network having an improved architecture is trained at the word level with an improved method, which, along with preprocessing improvements, results in a recognizer with greater recognition accuracy. Preprocessing is performed on the input data and, for example, may include resampling the data with sample points based on the second derivative to focus the recognizer on areas of the input data where the slope change per time is greatest. The input data is segmented, featurized and fed to the time-delayed neural network which outputs a matrix of character scores per segment. The neural network architecture outputs a separate score for the start and the continuation of a character. A dynamic time warp (DTW) is run against dictionary words to find the most probable path through the output matrix for that word, and each word is assigned a score based on the least costly path that can be traversed through the output matrix. The word (or words) with the overall lowest score (or scores) are returned. A DTW is similarly used in training, whereby the sample ink only need be labeled at the word level.

Handwriting and speech recognizer using neural network with separate start and continuation output scores

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Woodinville, WA, United States of America

Angshuman Guha