Robust industrial optical character recognition
First Claim
1. A method of operation in an image processor system for recognizing one or more characters in an image of a target object, the method comprising:
- receiving, by a processor, image data representing an image of the target object;
generating histogram of oriented gradients (HOG) features for the received image data;
accessing, by the processor, a plurality of HOG character models stored in a nontransitory processor-readable storage medium, each of the plurality of HOG character models associated with a respective known character;
for each of a plurality of image locations of the image, comparing each of the plurality of HOG character models to the generated HOG features of the image at the image location;
determining a quality measure based at least in part on the comparing of each of the plurality of HOG character models to the generated HOG features of the image; and
logically associating in a nontransitory processor-readable storage medium at least one known character with at least one image location based on the comparison to generate at least one candidate character location.
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Abstract
Systems, methods, and articles to provide robust optical character recognition (OCR) for use in industrial environments. One or more implementations include utilizing Histogram of Oriented Gradients (HOG) features with a sliding window approach as a robust and computationally efficient method of OCR. The implementations are relatively simple to use because there are relatively few parameters to adjust, which allows a non-expert user to setup or modify the system and achieve desirable performance. One reason this is possible is because the implementations described herein do not require character segmentation, which can be difficult to optimize.
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Citations
38 Claims
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1. A method of operation in an image processor system for recognizing one or more characters in an image of a target object, the method comprising:
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receiving, by a processor, image data representing an image of the target object; generating histogram of oriented gradients (HOG) features for the received image data; accessing, by the processor, a plurality of HOG character models stored in a nontransitory processor-readable storage medium, each of the plurality of HOG character models associated with a respective known character; for each of a plurality of image locations of the image, comparing each of the plurality of HOG character models to the generated HOG features of the image at the image location; determining a quality measure based at least in part on the comparing of each of the plurality of HOG character models to the generated HOG features of the image; and logically associating in a nontransitory processor-readable storage medium at least one known character with at least one image location based on the comparison to generate at least one candidate character location. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A method of operation in an image processor system for recognizing one or more characters in an image of a target object, the method comprising:
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receiving, by a processor, image data representing an image of the target object; generating histogram of oriented gradients (HOG) features for the received image data; accessing, by the processor, a plurality of HOG character models stored in a nontransitory processor-readable storage medium, each of the plurality of HOG character models associated with a respective known character; for each of a plurality of image locations of the image, comparing each of the plurality of HOG character models to the generated HOG features of the image at the image location; and logically associating in a nontransitory processor-readable storage medium at least one known character with at least one image location based on the comparison to generate at least one candidate character location, wherein each of the plurality of HOG character models comprises a plurality of bins each having a spatial dimension, and generating histogram of oriented gradients (HOG) features for the received image data comprises dividing the received image data into a plurality of bins each having a spatial dimension that is equal to the spatial dimension of the bins associated with the HOG character models. - View Dependent Claims (11)
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12. A method of operation in an image processor system for recognizing one or more characters in an image of a target object, the method comprising:
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receiving, by a processor, image data representing an image of the target object; generating histogram of oriented gradients (HOG) features for the received image data; accessing, by the processor, a plurality of HOG character models stored in a nontransitory processor-readable storage medium, each of the plurality of HOG character models associated with a respective known character; for each of a plurality of image locations of the image, comparing each of the plurality of HOG character models to the generated HOG features of the image at the image location, wherein comparing each of the plurality of HOG character models to the generated HOG features of the image at the image location comprises, for each of a plurality of image locations of the image, correlating each of the plurality of HOG character models with the HOG features at the image location to generate a correlation value for each of the HOG character models; and logically associating in a nontransitory processor-readable storage medium at least one known character with at least one image location based on the comparison to generate at least one candidate character location; for each of the plurality of image locations, selecting one of the plurality of HOG character models having the highest correlation value with the image location; logically associating the highest correlation value of the selected HOG character model with the image location in a nontransitory processor-readable storage medium; and logically associating the known character associated with the selected HOG character model with the image location in a nontransitory processor-readable storage medium. - View Dependent Claims (13, 14, 15, 16, 17, 18)
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19. An image processor system, comprising:
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at least one processor; and at least one nontransitory processor-readable medium, communicatively coupled to the at least one processor and which stores histogram of oriented gradient (HOG) character models of a set of known characters and at least one of processor-executable instructions or data, each of the plurality of HOG character models comprises a plurality of bins each having a spatial dimension, wherein in use the at least one processor; receives image data that represents an image of a target object; divides the received image data into a plurality of bins each having a spatial dimension that is equal to the spatial dimension of the bins associated with the HOG character models; generates histogram of oriented gradients (HOG) features for the received image data; accesses the plurality of HOG character models stored in the at least one nontransitory processor-readable storage medium, each of the plurality of HOG character models associated with a respective known character; for each of a plurality of image locations of the image, compares each of the plurality of HOG character models to the generated HOG features of the image at the image location; and logically associates in the at least one nontransitory processor-readable storage medium at least one known character with at least one image location based on the comparison to generate at least one candidate character location. - View Dependent Claims (20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33)
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34. A method of operation in an image processor system for recognizing one or more words in an image of a target object, the method comprising:
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receiving, by at least one processor, training image data representing a plurality of training characters; generating a plurality of histogram of oriented gradients (HOG) character models, each of the plurality of HOG character models associated with one of the training characters; receiving, by at least one processor, image data representing an image of the target object; generating HOG features for the received image data; determining correlations between each of the HOG character models and the HOG features for the received image data; determining a plurality of candidate character locations based on the determined correlations; and assembling at least one word using at least some of the determined plurality of candidate character locations. - View Dependent Claims (35, 36, 37, 38)
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Specification