DIFFERENTIAL CLASSIFICATION USING MULTIPLE NEURAL NETWORKS
First Claim
1. A method comprising:
- storing a plurality of neural networks in memory, wherein each neural network of the plurality of neural networks is trained to recognize a set of confused graphemes from one or more sets of confused graphemes identified in recognition data for a plurality of document images, wherein each set of confused graphemes from the one or more sets of confused graphemes comprises a plurality of different graphemes that are graphically similar to each other;
receiving an input grapheme image associated with a document image comprising a plurality of grapheme images;
determining a set of recognition options for the input grapheme image, wherein the set of recognition options comprises a set of target characters that are similar to the input grapheme image;
selecting, by a processing device, a first neural network from the plurality of neural networks, wherein the first neural network is trained to recognize a first set of confused graphemes, and wherein the first set of graphemes comprises at least a portion of the set of recognition options for the input grapheme image; and
determining a grapheme class for the input grapheme image using the selected first neural network.
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Abstract
A classification engine stores a plurality of neural networks in memory, where each neural network is trained to recognize a set of confused graphemes from one or more sets of confused graphemes identified in recognition data for a plurality of document images. The classification engine receives an input grapheme image associated with a document image comprising a plurality of graphemes, determines a set of recognition options for the input grapheme image, wherein the set of recognition options comprises a set of target characters that are similar to the input grapheme image, selects a first neural network from the plurality of neural networks, wherein the first neural network is trained to recognize a first set of confused graphemes, and wherein the first set of graphemes comprises at least a portion of the set of recognition options for the input grapheme image, and determines a grapheme class for the input grapheme image using the selected first neural network.
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Citations
20 Claims
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1. A method comprising:
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storing a plurality of neural networks in memory, wherein each neural network of the plurality of neural networks is trained to recognize a set of confused graphemes from one or more sets of confused graphemes identified in recognition data for a plurality of document images, wherein each set of confused graphemes from the one or more sets of confused graphemes comprises a plurality of different graphemes that are graphically similar to each other; receiving an input grapheme image associated with a document image comprising a plurality of grapheme images; determining a set of recognition options for the input grapheme image, wherein the set of recognition options comprises a set of target characters that are similar to the input grapheme image; selecting, by a processing device, a first neural network from the plurality of neural networks, wherein the first neural network is trained to recognize a first set of confused graphemes, and wherein the first set of graphemes comprises at least a portion of the set of recognition options for the input grapheme image; and determining a grapheme class for the input grapheme image using the selected first neural network. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A computing apparatus comprising:
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a memory to store instructions; and a processing device, operatively coupled to the memory, to execute the instructions, wherein the processing device is to; store a plurality of neural networks in memory, wherein each neural network of the plurality of neural networks is trained to recognize a set of confused graphemes from one or more sets of confused graphemes identified in recognition data for a plurality of document images, wherein each set from the one or more sets of confused graphemes comprises a plurality of different graphemes that are graphically similar to each other; receive an input grapheme image associated with a document image comprising a plurality of grapheme images; determine a set of recognition options for the input grapheme image, wherein the set of recognition options comprises a set of target characters that are similar to the input grapheme image; select a first neural network from the plurality of neural networks, wherein the first neural network is trained to recognize a first set of confused graphemes, and wherein the first set of graphemes comprises at least a portion of the set of recognition options for the input grapheme image; and determine a grapheme class for the input grapheme image using the selected first neural network. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. A non-transitory computer readable storage medium, having instructions stored therein, which when executed by a processing device of a computer system, cause the processing device to perform operations comprising:
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storing a plurality of neural networks in memory, wherein each neural network of the plurality of neural networks is trained to recognize a set of confused graphemes from one or more sets of confused graphemes identified in recognition data for a plurality of document images, wherein each set from the one or more sets of confused graphemes comprises a plurality of different graphemes that are graphically similar to each other; receiving an input grapheme image associated with a document comprising a plurality of graphemes; determining a set of recognition options for the input grapheme image, wherein the set of recognition options comprises a set of target characters that are similar to the input grapheme image; selecting, by the processing device, a first neural network from the plurality of neural networks, wherein the first neural network is trained to recognize a first set of confused graphemes, and wherein the first set of graphemes comprises at least a portion of the set of recognition options for the input grapheme image; and determining a grapheme class for the input grapheme image using the selected first neural network. - View Dependent Claims (16, 17, 18, 19, 20)
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Specification