CPC G06V 30/18171 (2022.01) [G06F 16/313 (2019.01); G06F 16/35 (2019.01); G06F 16/387 (2019.01); G06F 18/2411 (2023.01); G06N 20/10 (2019.01); G06V 10/70 (2022.01); G06V 10/75 (2022.01); G06V 10/806 (2022.01); G06V 20/62 (2022.01); G06V 30/144 (2022.01); G06V 30/153 (2022.01); G06V 30/158 (2022.01); G06V 30/1916 (2022.01); G06V 30/19173 (2022.01); G06V 30/413 (2022.01); G06V 30/414 (2022.01); G06V 30/10 (2022.01)] | 20 Claims |
1. A method for visual recognition, comprising:
receiving an output of an optical character recognition process comprising a recognized block, the recognized block comprising a set of positional properties and recognized text;
creating a block weight from the set of positional properties for the recognized block;
creating a set of block tri-character grams for the recognized block based on the recognized text for the recognized block;
creating a block tri-character gram feature vector based on the set of block tri-character grams;
creating a block feature vector for the recognized block from the block weight and the block tri-character gram feature vector for the recognized block;
creating a feature vector based on the block feature vector;
retrieving a set of results from an index, wherein the index includes entries for documents, and further wherein the set of results is retrieved based on the feature vector; and
submitting the feature vector to a trained machine classifier,
wherein retrieving the set of results from the index based on the feature vector occurs responsive to the trained machine classifier classifying the feature vector into a designated category.
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