IMAGE-BASED BARCODE DECODING

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.

2 . The system of claim 1 , wherein the machine-readable label is a barcode.

3 . 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.

4 . 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.

5 . 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.

6 . The system of claim 5 , wherein the segmentation model comprises an attention mechanism.

7 . The system of claim 1 , wherein the predicted sequence of values is an alphanumeric representation of the predicted item identifier.

8 . 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.

9 . The system of claim 1 , further comprising:

adding the item represented by the item identifier to a shopping list associated with a user.

10 . 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.

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.

12 . The non-transitory computer-readable medium of claim 11 , wherein the machine-readable label is a barcode.

13 . 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.

14 . 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.

15 . 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.

16 . The non-transitory computer-readable medium of claim 15 , wherein the segmentation model comprises an attention mechanism.

17 . The non-transitory computer-readable medium of claim 11 , wherein the predicted sequence of values is an alphanumeric representation of the predicted item identifier.

18 . 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.

19 . 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.

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.