Unified digital ink recognition
First Claim
1. A computer-readable storage 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 digital ink training data corresponds to digital ink representative of at least two different types of digital ink input, each of the plurality of samples belong to one of a plurality of classes having combined feature data, and each of the plurality of samples is associated with a label comprising a recognition value;
b) processing a feature dataset of the selected sample into a recognition model by adjusting the combined feature data of the class to which the selected sample belongs and maintaining data representative of the features extracted from the selected sample in association with the recognition value associated with the selected sample;
c) selecting another sample from the plurality of samples and repeating steps a) and b) until each sample of the plurality of samples has been processed; and
d) providing a unified recognizer that recognizes an input item of one of the at least two different types of digital ink input without mode selection or recognition parameter input, the input item being recognized by extracting features of the input item and determining a matching class having combined feature data that best match features of the input item, and outputting a matching recognition value associated with the matching class.
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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 storage 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 digital ink training data corresponds to digital ink representative of at least two different types of digital ink input, each of the plurality of samples belong to one of a plurality of classes having combined feature data, and each of the plurality of samples is associated with a label comprising a recognition value; b) processing a feature dataset of the selected sample into a recognition model by adjusting the combined feature data of the class to which the selected sample belongs and maintaining data representative of the features extracted from the selected sample in association with the recognition value associated with the selected sample; c) selecting another sample from the plurality of samples and repeating steps a) and b) until each sample of the plurality of samples has been processed; and d) providing a unified recognizer that recognizes an input item of one of the at least two different types of digital ink input without mode selection or recognition parameter input, the input item being recognized by extracting features of the input item and determining a matching class having combined feature data that best match features of the input item, and outputting a matching recognition value associated with the matching class. - 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 training data corresponding to training samples that represent at least two different types of digital ink data to be recognized by building a classifier according to the at least two different types of digital ink data to be recognized, the training samples belonging to a plurality of classes having features; 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 the training samples in association with a recognition value of each class of the training samples; and an evaluation mechanism that evaluates the recognition model with respect to digital ink testing 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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determining a dataset having digital ink items representative of a plurality of different classes of digital ink data to be recognized; building a classifier according to the plurality of different classes of the digital ink data to be recognized by extracting features for the plurality of different classes of the digital ink data from samples of the plurality of different classes; using features of the classifier corresponding to the digital ink items to build a unified digital ink recognizer for two or more different types of the digital ink data; receiving an input item; and recognizing the input item with the unified digital ink recognizer to output a value associated with one of the two or more different types of the digital ink data. - View Dependent Claims (17, 18, 19, 20)
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Specification