Abstract: Segmentation first breaks the images into segments or regions, with the segments of the region having text or symbols. The segmented image is separately applied to two different CNN-based models. Each model produces text boxes where potential text might exist. Then, a selective NMS algorithm is applied to the output of each model to produce a final group of text regions. These text regions are analyzed and actions are taken.

Abstract: Segmentation first breaks the images into segments or regions, with the segments of the region having text or symbols. The segmented image is separately applied to two different CNN-based models. Each model produces text boxes where potential text might exist. Then, a selective NMS algorithm is applied to the output of each model to produce a final group of text regions. These text regions are analyzed and actions are taken.

Abstract: An automated planogram anomaly detection solution rapidly and reliably identifies mismatches between planograms and actual item placement. Examples receive a real time (RT) image of a shelf unit corresponding to at least a first portion of a planogram; detect, within the RT image, item boundaries for a plurality of items on the shelf unit and tag boundaries for a plurality of tags associated with the shelf unit; extract text from at least one tag; extract attributes from at least one item; map the extracted item attributes with the extracted tag text; detect, based at least on the map, a planogram anomaly; and based at least on detecting the planogram anomaly, generate a report identifying the planogram anomaly (e.g., a mismatch between a tag and an item). Some examples compare the RT image with a ground truth (GT) image to detect anomalies, for example empty space on the shelf unit.

Abstract: Segmentation first breaks the images into segments or regions, with the segments of the region having text or symbols. The segmented image is separately applied to two different CNN-based models. Each model produces text boxes where potential text might exist. Then, a selective NMS algorithm is applied to the output of each model to produce a final group of text regions. These text regions are analyzed and actions taken.

Abstract: Segmentation first breaks the images into segments or regions, with the segments of the region having text or symbols. The segmented image is separately applied to two different CNN-based models. Each model produces text boxes where potential text might exist. Then, a selective NMS algorithm is applied to the output of each model to produce a final group of text regions. These text regions are analyzed and actions taken.

Abstract: An automated planogram anomaly detection solution rapidly and reliably identifies mismatches between planograms and actual item placement. Examples receive a real time (RT) image of a shelf unit corresponding to at least a first portion of a planogram; detect, within the RT image, item boundaries for a plurality of items on the shelf unit and tag boundaries for a plurality of tags associated with the shelf unit; extract text from at least one tag; extract attributes from at least one item; map the extracted item attributes with the extracted tag text; detect, based at least on the map, a planogram anomaly; and based at least on detecting the planogram anomaly, generate a report identifying the planogram anomaly (e.g., a mismatch between a tag and an item). Some examples compare the RT image with a ground truth (GT) image to detect anomalies, for example empty space on the shelf unit.