# US 12,762,365 B2 · Image-based barcode decoding > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,762,365 B2 - Document ID: US12762365B2 - Status: Granted - Kind: B2 - Application number: 19/342,637 - Filed: 2025-09-28 - Published: 2026-01-29 - Granted: 2026-10-13 - Earliest claimed: 2021-03-24 - Assignees: Maplebear Inc. - Applicants: Maplebear Inc. - Inventors: Shiyuan Yang; Yilin Huang; Wentao Pan; Xiao Zhou - Web page: https://patentgrep.com/patent/US12762365B2 ## CPC - G06K 7/1413: {1D bar codes} - G06T 7/10: Segmentation; Edge detection (motion-based segmentation G06T7/215) - G06F 2218/12: Classification; Matching - G06T 2207/20081: Training; Learning - G06K: GRAPHICAL DATA READING (image or video recognition or understanding G06V); PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract A barcode decoding system decodes item identifiers from images of barcodes. The barcode decoding system receives an image of a barcode and rotates the image to a pre-determined orientation. The barcode decoding system also may segment the barcode image to emphasize the portions of the image that correspond to the barcode. The barcode decoding system generates a binary sequence representation of the item identifier encoded in the barcode by applying a barcode classifier model to the barcode image, and decodes the item identifier from the barcode based on the binary sequence representation. ## Claims ### Claim 1 A system comprising: a processor; and a non-transitory computer-readable medium storing instruction that, when executed by the processor, cause the processor to perform operations comprising: receiving, from a camera coupled to the system, an image depicting a machine-readable label, wherein the machine-readable label represents an item identifier for an item comprising a sequence of values encoded in portions of the machine-readable label, wherein each portion corresponds to a encoding of a value of the sequence of values of the item identifier; generating a sequence representation of the item identifier for the item by applying a machine-readable label prediction model to the image of the machine-readable label, wherein the sequence representation represents a prediction of the sequence of values, wherein the sequence representation represents one or more probabilistic predictions of the sequence of values encoded in the portions of the machine-readable label, and wherein the machine-readable label prediction model is a machine-learning model stored on the computer-readable medium and trained to generate sequence representations of item identifiers for machine-readable labels in images; and decoding the item identifier represented in the machine-readable label from the sequence representation of the item identifier generated by the machine-readable label prediction model. ### Claim 2 (depends on 1) The system of claim 1, wherein the machine-readable label is a barcode. ### Claim 3 (depends on 1) The system of claim 1, wherein the machine-readable label prediction model generates the prediction of the sequence of values by applying a connectionist temporal classification inference algorithm to the image. ### Claim 4 (depends on 1) The system of claim 1, wherein the operations further comprise: generating a rotated image of the machine-readable label by applying a rotation model to the image, wherein the rotation model comprises a machine-learning model stored on the computer-readable medium and trained to rotate images depicting machine-readable labels to a pre-determined orientation; and generating the sequence representation based on the rotated image. ### Claim 5 (depends on 1) The system of claim 1, wherein the operations further comprise: generating a segmented image of the machine-readable label by applying a segmentation model to the image, wherein the segmentation model comprises a machine-learning model stored on the computer-readable medium and trained to remove backgrounds from images of machine-readable labels; and generating the sequence representation based on the segmented image. ### Claim 6 (depends on 5) The system of claim 5, wherein the segmentation model comprises an attention mechanism. ### Claim 7 (depends on 1) The system of claim 1, wherein the predicted sequence of values is an alphanumeric representation of the predicted item identifier. ### Claim 8 (depends on 1) The system of claim 1, wherein the predicted sequence of values comprises a set of binary predictions that indicate probabilistic predictions of corresponding binary values of a binary encoding of the item identifier. ### Claim 9 (depends on 1) The system of claim 1, further comprising: adding the item represented by the item identifier to a shopping list associated with a user. ### Claim 10 (depends on 1) The system of claim 1, further comprising: causing information associated with the item to be displayed on a display device coupled to a shopping cart. ### Claim 11 A non-transitory computer-readable medium storing instruction that, when executed by a processor, cause the processor to perform operations comprising: receiving, from a camera, an image depicting a machine-readable label, wherein the machine-readable label represents an item identifier for an item comprising a sequence of values encoded in portions of the machine-readable label, wherein each portion corresponds to a encoding of a value of the sequence of values of the item identifier; generating a sequence representation of the item identifier for the item by applying a machine-readable label prediction model to the image of the machine-readable label, wherein the sequence representation represents a prediction of the sequence of values, wherein the sequence representation represents one or more probabilistic predictions of the sequence of values encoded in the portions of the machine-readable label, and wherein the machine-readable label prediction model is a machine-learning model stored on the computer-readable medium and trained to generate sequence representations of item identifiers for machine-readable labels in images; and decoding the item identifier represented in the machine-readable label from the sequence representation of the item identifier generated by the machine-readable label prediction model. ### Claim 12 (depends on 11) The non-transitory computer-readable medium of claim 11, wherein the machine-readable label is a barcode. ### Claim 13 (depends on 11) The non-transitory computer-readable medium of claim 11, wherein the machine-readable label prediction model generates the prediction of the sequence of values by applying a connectionist temporal classification inference algorithm to the image. ### Claim 14 (depends on 11) The non-transitory computer-readable medium of claim 11, wherein the operations further comprise: generating a rotated image of the machine-readable label by applying a rotation model to the image, wherein the rotation model comprises a machine-learning model stored on the computer-readable medium and trained to rotate images depicting machine-readable labels to a pre-determined orientation; and generating the sequence representation based on the rotated image. ### Claim 15 (depends on 11) The non-transitory computer-readable medium of claim 11, wherein the operations further comprise: generating a segmented image of the machine-readable label by applying a segmentation model to the image, wherein the segmentation model comprises a machine-learning model stored on the computer-readable medium and trained to remove backgrounds from images of machine-readable labels; and generating the sequence representation based on the segmented image. ### Claim 16 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein the segmentation model comprises an attention mechanism. ### Claim 17 (depends on 11) The non-transitory computer-readable medium of claim 11, wherein the predicted sequence of values is an alphanumeric representation of the predicted item identifier. ### Claim 18 (depends on 11) The non-transitory computer-readable medium of claim 11, wherein the predicted sequence of values comprises a set of binary predictions that indicate probabilistic predictions of corresponding binary values of a binary encoding of the item identifier. ### Claim 19 (depends on 11) The non-transitory computer-readable medium of claim 11, further comprising: adding the item represented by the item identifier to a shopping list associated with a user. ### Claim 20 A method comprising: receiving, from a camera, an image depicting a machine-readable label, wherein the machine-readable label represents an item identifier for an item comprising a sequence of values encoded in portions of the machine-readable label, wherein each portion corresponds to a encoding of a value of the sequence of values of the item identifier; generating a sequence representation of the item identifier for the item by applying a machine-readable label prediction model to the image of the machine-readable label, wherein the sequence representation represents a prediction of the sequence of values, wherein the sequence representation represents one or more probabilistic predictions of the sequence of values encoded in the portions of the machine-readable label, and wherein the machine-readable label prediction model is a machine-learning model and trained to generate sequence representations of item identifiers for machine-readable labels in images; and decoding the item identifier represented in the machine-readable label from the sequence representation of the item identifier generated by the machine-readable label prediction model.