On-Shelf Image Based Barcode Reader for Inventory Management System

A non-transitory computer-readable storage medium comprising memory with executable computer instructions encoded thereon that, when executed by one or more processors, cause the one or more processors to perform operations, the instructions comprising instructions to:

• receive a plurality of low-resolution images, each of the images including at least a portion of a product, the plurality of low-resolution images having resolution lower than a predetermined threshold;
• pre-process the low-resolution images resulting in a plurality of pre-processed images;
• identify, in each respective one of the plurality of pre-processed images, a first bounding polygon around the product, and a second bounding polygon around an identifier corresponding to the product;
• apply at least the second bounding polygons from each of the pre-processed images to a machine-learned model and receiving as output from the machine-learned model a high-resolution crop of the identifier;
• decode the identifier;
• register the identifier to the first bounding polygon and to a location associated with one or more cameras that captured the plurality of low-resolution images;
• receive a request from a client device to locate a product associated with the identifier;
• and
• in response to receiving the request, providing the location associated with the one or more cameras to the client device.

A computing system comprising:

• a processor;
• and
• a non-transitory computer-readable storage medium storing instructions for managing inventory visibility, the instructions when executed by the processor cause the processor to perform steps including:
• receiving a plurality of low-resolution images, each of the images including at least a portion of a product, the plurality of low-resolution images having resolution lower than a predetermined threshold;
• pre-processing the low-resolution images resulting in a plurality of pre-processed images;
• identifying, in each respective one of the plurality of pre-processed images, a first bounding polygon around the product, and a second bounding polygon around an identifier corresponding to the product;
• applying at least the second bounding polygons from each of the pre-processed images to a machine-learned model and receiving as output from the machine-learned model a high-resolution crop of the identifier;
• decoding the identifier;
• registering the identifier to the first bounding polygon and to a location associated with one or more cameras that captured the plurality of low-resolution images;
• receiving a request from a client device to locate a product associated with the identifier;
• and
• in response to receiving the request, providing the location associated with the one or more cameras to the client device.