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US 12,749,329 B2Granted
Anomaly detection in documents with visual cues
AssigneeOracle Financial Services Software Limited
InventorsKrishna Sameera Ellendula, Utkarsh Sharma, Veresh Jain
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Abstract
Techniques are disclosed for understanding the visual structure and patterns of documents and detecting anomalies in data of the documents based on the understanding of the visual structure and patterns of the documents. In one aspect, a computer-implemented method is provided that includes accessing a set of documents, converting the set of documents to a set of images in a binary format, generating a common feature template based on the set of images, comparing each image from the set of images to the common feature template to identify images with at least one anomalous feature, and outputting the images with at least one anomalous feature.
Claims20
A computer-implemented method comprising:
- accessing a set of documents;
- converting the set of documents to a set of images in a binary format;
- generating a common feature template based on the set of images, wherein the generating comprises:
- generating a pixel map based on the set of images, and
- filtering low intensity pixels out of the pixel map to generate the common feature template;
- comparing each image from the set of images to the common feature template to identify images with at least one anomalous feature, wherein the comparing each image comprises:
- determining whether a difference in intensity exists between each pixel in each image and a same pixel in the common feature template,
- counting each pixel in each image that is different in intensity from the same pixel in the common feature template,
- determining whether a count of pixels that are different in intensity for each feature among a plurality of features in each image is greater than a predetermined pixel count threshold,
- when the count of pixels that are different in intensity for a given feature of the plurality of features in the image is greater than the predetermined pixel count threshold, determining whether the given feature is a common difference feature in at least a predetermined threshold number of the images,
- in response to the determining that the given feature is the common difference feature in at least the predetermined threshold number of the images, filtering out the common difference feature from the images of the set of images, and
- in response to the determining that the given feature is not the common difference feature in at least the predetermined threshold number of the images,
- identifying the given feature as an anomalous feature;
- and
- outputting the images with the at least one anomalous feature among the plurality of features.
12 more elements
A system comprising:
- one or more processors;
- and
- one or more computer-readable media storing instructions which, when executed by the one or more processors, cause the one or more processors to perform operations including:
- accessing a set of documents;
- converting the set of documents to a set of images in a binary format;
- generating a common feature template based on the set of images, wherein the generating includes:
- generating a pixel map based on the set of images, and
- filtering low intensity pixels out of the pixel map to generate the common feature template;
- comparing each image from the set of images to the common feature template to identify images with at least one anomalous feature, wherein the comparing each image includes:
- determining whether a difference in intensity exists between each pixel in each image and a same pixel in the common feature template,
- counting each pixel in each image that is different in intensity from the same pixel in the common feature template,
- determining whether a count of pixels that are different in intensity for each feature among a plurality of features in each image is greater than a predetermined pixel count threshold,
- when the count of pixels that are different in intensity for a given feature of the plurality of features in the image is greater than the predetermined pixel count threshold, determining whether the given feature is a common difference feature in at least a predetermined threshold number of the images,
- in response to the determining that the given feature is the common difference feature in at least the predetermined threshold number of the images, filtering out the common difference feature from the images of the set of images, and
- in response to the determining that the given feature is not the common difference feature in at least the predetermined threshold number of the images,
- identifying the given feature as an anomalous feature;
- and
- outputting the images with the at least one anomalous feature among the plurality of features.
15 more elements
One or more non-transitory computer-readable media storing instructions which, when executed by one or more processors, cause the one or more processors to perform operations including:
- accessing a set of documents;
- converting the set of documents to a set of images in a binary format;
- generating a common feature template based on the set of images, wherein the generating includes:
- generating a pixel map based on the set of images, and
- filtering low intensity pixels out of the pixel map to generate the common feature template;
- comparing each image from the set of images to the common feature template to identify images with at least one anomalous feature, wherein the comparing each image includes:
- determining whether a difference in intensity exists between each pixel in each image and a same pixel in the common feature template,
- counting each pixel in each image that is different in intensity from the same pixel in the common feature template,
- determining whether a count of pixels that are different in intensity for each feature among a plurality of features in each image is greater than a predetermined pixel count threshold,
- when the count of pixels that are different in intensity for a given feature of the plurality of features in the image is greater than the predetermined pixel count threshold, determining whether the given feature is a common difference feature in at least a predetermined threshold number of the images,
- in response to the determining that the given feature is the common difference feature in at least the predetermined threshold number of the images, filtering out the common difference feature from the images of the set of images, and
- in response to the determining that the given feature is not the common difference feature in at least the predetermined threshold number of the images,
- identifying the given feature as an anomalous feature;
- and
- outputting the images with the at least one anomalous feature among the plurality of features.
12 more elements
Dependencies are inferred from the claim text.