Systems and methods are provided for: receiving an image containing a code that has one or more visual qualities that fail to satisfy respective thresholds; applying a trained machine learning model to find a rough location of the code by generating a bounding box and cropping out the portion of the image; applying another trained machine learning model to the portion of the image to estimate key point locations of the code depicted in the portion of the image, aligning the portion of the image that depicts the code based on the estimated key point locations; and decoding, by the other trained machine learning model, the aligned portion of the image that depicts the code.

A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform facilitating display of instructions to a user interface of a mobile device of a user on how to rotate a medication package in front of a camera of the mobile device to capture a video of a rotated view of a non-planar surface of the medication package; capturing, using the camera, the video of the rotated view of the non-planar surface; generating, using an input pre-processing algorithm, a series of images from the video; recognizing, using an output post-processing algorithm, respective words from the respective machine-readable text data; merging, using a merging algorithm, the respective words from the images to create lines of text; and extracting, using a machine learning algorithm, prescription data associated with prescription data fields from the lines of text. Other embodiments are described.

A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform facilitating display of instructions to a user interface of a mobile device of a user on how to rotate a medication package in front of a camera of the mobile device to capture a video of a rotated view of a non-planar surface of the medication package; capturing, using the camera, the video of the rotated view of the non-planar surface; generating, using an input pre-processing algorithm, a series of images from the video; recognizing, using an output post-processing algorithm, respective words from the respective machine-readable text data; merging, using a merging algorithm, the respective words from the images to create lines of text; and extracting, using a machine learning algorithm, prescription data associated with prescription data fields from the lines of text. Other embodiments are described.

To suppress an increase in processing time due to a load of inference processing while improving reading accuracy by the inference processing of machine learning. An optical information reading device includes a processor including: an inference processing part that inputs a code image to a neural network and executes inference processing of generating an ideal image corresponding to the code image; and a decoding processing part that executes first decoding processing of decoding the code image and second decoding processing of decoding the ideal image generated by the inference processing part. The processor executes the inference processing and the first decoding processing in parallel, and executes the second decoding processing after completion of the inference processing.

Systems and methods are provided for: receiving an image containing a code that has one or more visual qualities that fail to satisfy respective thresholds; applying a trained machine learning model to find a rough location of the code by generating a bounding box and cropping out the portion of the image; applying another trained machine learning model to the portion of the image to estimate key point locations of the code depicted in the portion of the image, aligning the portion of the image that depicts the code based on the estimated key point locations; and decoding, by the other trained machine learning model, the aligned portion of the image that depicts the code.

The present invention relates to a system and method for optimizing the placement, size, scannability, and effectiveness of optical labels, such as 2D barcodes or machine-readable labels like QR codes, on a specified medium. The system comprises a set of servers configured to execute an artificial intelligence (AI) algorithm, a set of user devices, and a database. The AI algorithm analyzes uploaded images of intended mediums for optical label placement, determines optimal placement, size, and orientation of the optical labels, and calculates individual scores for visibility, scannability, and likelihood of being noticed by potential users for each suggestion, then combines these into an overall readability score. The invention offers a user-friendly, efficient, and objective approach to optimizing optical label placement that is particularly effective for addressing considerations for physical object mediums.

A barcode reader configured to capture interleaved frame types optimized for vision capture and barcode capture are disclosed herein. An example barcode reader is configured to operate in a pre-determined repetitive pattern of capturing a first frame and capturing a second frame over a reading cycle having a fixed duration after a triggering event, wherein the first frame is captured over a first exposure period having a first duration, and the second frame is captured over a second exposure period having a second duration, and wherein the first frame is associated with a first brightness parameter, and the second frame is associated with a second brightness parameter.

Multiple field of view (FOV) systems are disclosed herein. An example system includes a bioptic barcode reader having a target imaging region. The bioptic barcode reader includes at least one imager having a first FOV and a second FOV and is configured to capture an image of a target object from each FOV. The example system includes one or more processors configured to receive the images and a trained object recognition model stored in memory communicatively coupled to the one or more processors. The memory includes instructions that, when executed, cause the one or more processors to analyze the images to identify at least a portion of a barcode and one or more features associated with the target object. The instructions further cause the one or more processors to determine a target object identification probability and to determine whether a predicted product identifies the target object.

A system for verifying a machine-readable label comprises a scan table processing device comprising a first input for receiving a list of items with machine-readable labels; a second input for receiving a list of stores that have an inventory of the items in the list of items and that have at least one sensing device for capturing images of the items; and an output that includes a plurality of electronic records. The system further comprises a data repository that stores the captured images of the items and that updates the electronic records to include an association to the captured images; a graphical user interface (GUI) processing apparatus that modifies the captured images in preparation for training an artificial intelligence apparatus to identify the items in the images; and a machine language (ML) model processor that determines whether the images training the artificial intelligence apparatus are correctly identified with machine-readable labels associated with the items.

A barcode reader configured to capture interleaved frame types optimized for vision capture and barcode capture are disclosed herein. An example barcode reader is configured to operate in a pre-determined repetitive pattern of capturing a first frame and capturing a second frame over a reading cycle having a fixed duration after a triggering event, wherein the first frame is captured over a first exposure period having a first duration, and the second frame is captured over a second exposure period having a second duration, and wherein the first frame is associated with a first brightness parameter, and the second frame is associated with a second brightness parameter.