SYSTEM AND METHOD FOR SCENE TEXT RECOGNITION
First Claim
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1. A character-recognition method for discriminating between a plurality of character classes, the method comprising:
- using a processor,defining a plurality of irregularly sized and positioned sub-regions within an image region of specified image dimensions;
for each of the plurality of character classes, determining a discriminative feature space bycomputing, for each of a plurality of training images within a set of training images having the specified image dimensions and being associated with the character class, mid-level features for all of the sub-regions;
computing feature weights for the mid-level features using a machine-learning algorithm applied to the set of training images;
ranking the mid-level features based on the weights; and
selecting a number of top-ranked features for inclusion in the discriminative feature space; and
creating a combined feature space from the discriminative feature spaces for all of the character classes.
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Abstract
Apparatus and method for performing accurate text recognition of non-simplistic images (e.g., images with clutter backgrounds, lighting variations, font variations, non-standard perspectives, and the like) may employ a machine-learning approach to identify a discriminative feature set selected from among features computed for a plurality of irregularly positioned, sized, and/or shaped (e.g., randomly selected) image sub-regions.
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Citations
20 Claims
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1. A character-recognition method for discriminating between a plurality of character classes, the method comprising:
using a processor, defining a plurality of irregularly sized and positioned sub-regions within an image region of specified image dimensions; for each of the plurality of character classes, determining a discriminative feature space by computing, for each of a plurality of training images within a set of training images having the specified image dimensions and being associated with the character class, mid-level features for all of the sub-regions; computing feature weights for the mid-level features using a machine-learning algorithm applied to the set of training images; ranking the mid-level features based on the weights; and selecting a number of top-ranked features for inclusion in the discriminative feature space; and creating a combined feature space from the discriminative feature spaces for all of the character classes. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A system comprising:
a plurality of modules, comprising one or more processors, comprising; a sub-region-selection module configured to define a plurality of irregularly sized and positioned sub-regions within an image region; a feature-computation module configured to compute, from an input image, mid-level features for each of a specified plurality of sub-regions; a machine-learning module configured to determine, from mid-level features computed for a plurality of input images of a set of training images associated with a character class, feature weights associated with the character class; and a feature-selection module configured to rank features for a character class based on the feature weights associated with the class, and to select a number of top-ranked features for inclusion in a discriminative feature space. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19)
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20. A non-transitory machine-readable medium comprising a plurality of machine-executable instructions configured to cause one or more processors to:
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define a plurality of irregularly sized and positioned sub-regions within an image region of specified image dimensions; for each of the plurality of character classes, determine a discriminative feature space by computing, for each of a plurality of training images within a set of training images having the specified image dimensions and being associated with the character class, mid-level features for all of the sub-regions; computing feature weights for the mid-level features using a machine-learning algorithm applied to the set of training images; ranking the mid-level features based on the weights; and and selecting a number of top-ranked features for inclusion in the discriminative feature space; and create a combined feature space from the discriminative feature spaces for all of the character classes.
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Specification