Unifield digital ink recognition
First Claim
1. A computer-readable medium having computer-executable instructions, which when executed perform steps, comprising:
- a) extracting features from a selected sample of a plurality of samples of digital ink training data, wherein the training data corresponds to digital ink representative of at least two different types of digital ink input, and the selected sample is associated with a recognition value as its label;
b) processing a feature dataset of the selected sample into a recognition model including by adjusting the combined feature data of the class to which the selected sample belongs and maintaining data representative of the features in association with the recognition value;
c) selecting another sample from the plurality and repeating steps a) and b) until each sample of the plurality has been processed; and
d) providing a unified recognizer that recognizes an input item including between the two different types of digital ink input without mode selection or recognition parameter input, including by extracting features of the input item and determining which data representative of the features of a class the features of the input item best match, and outputting the recognition value associated with that data.
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Accused Products
Abstract
Described is a unified digital ink recognizer that recognizes various different types of digital ink data, such as handwritten character data and custom data, e.g., sketched shapes, handwritten gestures, and/or drawn pictures, without further participation by a user such as recognition mode selection or parameter input. For a custom item, the output may be a Unicode value from a private use area of Unicode. Building the unified digital ink recognizer may include defining the data set to be recognized, extracting features of training samples corresponding to the dataset items to build a recognizer model, evaluating the recognizer model using testing data, and modifying the recognizer model using tuning data. The extracted features may be processed into feature data for a multi-dimensional nearest neighbor recognizer approach; the extracted features for the samples of each class is calculated and combined into the feature set for this class in the resulting recognizer model.
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Citations
20 Claims
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1. A computer-readable medium having computer-executable instructions, which when executed perform steps, comprising:
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a) extracting features from a selected sample of a plurality of samples of digital ink training data, wherein the training data corresponds to digital ink representative of at least two different types of digital ink input, and the selected sample is associated with a recognition value as its label; b) processing a feature dataset of the selected sample into a recognition model including by adjusting the combined feature data of the class to which the selected sample belongs and maintaining data representative of the features in association with the recognition value; c) selecting another sample from the plurality and repeating steps a) and b) until each sample of the plurality has been processed; and d) providing a unified recognizer that recognizes an input item including between the two different types of digital ink input without mode selection or recognition parameter input, including by extracting features of the input item and determining which data representative of the features of a class the features of the input item best match, and outputting the recognition value associated with that data. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. In a computing environment, a system comprising:
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a feature extraction mechanism that featurizes digital ink data corresponding to training samples that represent at least two different types of digital ink data; a recognition model builder mechanism coupled to the feature extraction mechanism that builds a recognition model including by persisting data representative of the features of each class of training sample in association with a recognition value of that class of training sample; and an evaluation mechanism that evaluates the recognition model with respect to digital ink data corresponding to testing samples. - View Dependent Claims (11, 12, 13, 14, 15)
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16. In a computing environment, a method comprising:
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building a unified digital ink recognizer using two or more different types of digital ink data; receiving an input item; and recognizing the input item with the recognizer to output a value associated with one of the types of digital ink data. - View Dependent Claims (17, 18, 19, 20)
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Specification