Handwriting recognition using a comparative neural network
First Claim
1. A method of comparing inputs, comprising:
- receiving a first input;
receiving a second input;
ordering the first input with respect to the second input using a neural network.
2 Assignments
0 Petitions
Accused Products
Abstract
Handwriting recognition techniques employing a personalized handwriting recognition engine. The recognition techniques use examples of an individual'"'"'s previous writing style to help recognize new pen input from that individual. The techniques also employ a shape trainer to select samples of an individual'"'"'s handwriting that accurately represent the individual'"'"'s writing style, for use as prototypes to recognize subsequent handwriting from the individual. The techniques also alternately or additionally employ an intelligent combiner to combine the recognition results from the personalized recognition engine and the conventional recognition engine (or engines). The combiner may use a comparative neural network to combine the recognition results from multiple recognition engines. The combiner alternately may use a rule-based system based on prior knowledge of different recognition engines.
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Citations
11 Claims
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1. A method of comparing inputs, comprising:
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receiving a first input;
receiving a second input;
ordering the first input with respect to the second input using a neural network. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A method of training a neural network to compare a first input with a second input, comprising:
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(a) selecting an input x;
(b) identifying a correct classification a(x) for the input x;
(c) operating each of a plurality of base-classifiers to obtain an ordered list of alternative classifications of x from each base-classifier;
(d) merge the alternative lists together such that common classifications are coalesced;
(e) if the correct classification σ
(x) does not appear in the merged list, discarding the merged list;
(f) if the correct classification σ
(x) does appear in the merged list, then extracting context features C(x) from x, andcombining σ
(x) with all erroneous classifications σ
(x) in a combined list so as to create comparative training samples of the form (C(x), D(σ
(x)), D(λ
(x)),
0) or (C(x), D(λ
(x)), D(σ
(x)),
1) where D( ) is a function the maps classifications to their description features;
(g) executing steps (a)-(f) through one or more base-classifier training sets;
(h) collecting the generated comparative training samples into an single training set;
(i) shuffling the set; and
(j) training a comparative neural network with the set.
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8. A classification tool, comprising:
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a first classifier that produces a first classifier output;
a second classifier that produces a second classifier output; and
a comparative neural network that compares the classifier output with the second classifier output. - View Dependent Claims (9, 10, 11)
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Specification