Pallet tag classification for improved text recognition accuracy

Patent metadata

Field	Value
Publication number	US-2025200504-A1
Application number	US-202318537949-A
Country	US
Kind code	A1
Filing date	Dec 13, 2023
Priority date	Dec 13, 2023
Publication date	Jun 19, 2025
Grant date	—

Abstract

Examples provide for pallet classification and pallet tag text recognition. The system includes a pallet text manager that classifies a type of pallet tag based on detected lines of text in the pallet tag using a classification model. Qualified lines of text are selected from the detected lines of text based on the classification type and corresponding format of the text. Each qualified line of text is associated with a pallet attribute, such as a pallet identifier (ID), an item ID, or a date of creation of the pallet tag. Attribute values from the set of qualified lines of text are paired with location data for the current location of the pallet. The attribute values and the paired location data are saved in a pallet attribute table. The pallet attributes are used to identify the location of pallets in a retail facility with improved accuracy and efficiency.

First claim

What is claimed is:

1 . A system for pallet text recognition with improved accuracy, the system comprising:
a processor; and
a computer-readable medium storing instructions that are operative upon execution by the processor to:
detect a plurality of lines of text associated with an image of a pallet tag associated with a pallet, the plurality of lines of text associated with a first tag format; classify a type of the pallet tag based on the plurality of lines of text, the type of the pallet tag corresponding to the first tag format; select a set of qualified lines of text within the plurality of lines of text using the first tag format associated with the type of the pallet tag, a qualified line of text comprising a pallet attribute; extract a set of pallet attribute values from the set of qualified lines of text, the set of pallet attribute values comprising a pallet identifier (ID); and map the set of pallet attribute values to a location ID in a pallet entry within a pallet attributes table, wherein the location ID identifies a location of the pallet within a retail facility, and wherein the location of the pallet is presented to a user via a user interface device for improved accuracy of locating pallets within the retail facility.

2 . The system of claim 1 , wherein the instructions are further operative to:
detect a tilted line of text within the plurality of lines of text within a cropped image of a portion of the pallet tag; generate a plurality of text-related values associated with a plurality of text-related pixels in the image and a plurality of non-text related values associated with a plurality of non-text related pixels in the portion of the image, the plurality of text-related values corresponding to alphanumeric characters in the tilted line of text; and enclose the plurality of text-related values within a quadrilateral-shaped bounding box, wherein the quadrilateral-shaped bounding box encloses a plurality of lines of text detected on the pallet tag.

3 . The system of claim 1 , wherein the instructions are further operative to:
identify a set of disqualified lines of text within the plurality of lines of text filter the set of disqualified lines of text from the plurality of lines of text to reduce noise within the plurality of lines of text.

4 . The system of claim 1 , wherein the set of pallet attribute values comprises an item ID associated with an item, wherein the instructions are further operative to:
pair the item ID with the location of the pallet; and store the item ID paired with the location ID in the pallet entry within the pallet attributes table.

5 . The system of claim 1 , wherein the set of pallet attribute values comprises a date of creation of the pallet tag, wherein the instructions are further operative to:
pair the date of creation of the pallet tag with the location of the pallet; and store the date of creation paired with the location ID in the pallet entry within the pallet attributes table.

6 . The system of claim 1 , wherein the instructions are further operative to:
fine-tune a set of hyperparameters associated with a recognition model implemented on the processor, wherein the recognition model is trained on a first version of a data set, and wherein the recognition model is capable of analyzing pallet tags associated with a second version of the data set.

7 . The system of claim 1 , wherein the instructions are further operative to:
detect a second plurality of lines of text associated with a second pallet tag having a second tag format; classify the type of the second pallet tag corresponding to the second tag format; identify a second set of qualified lines of text within the second plurality of lines of text using the second tag format; and assign the second set of qualified lines of text to a second set of pallet attributes in the pallet attributes table, the set of pallet attribute values comprising a second pallet ID, an item ID, and a date of creation of the second pallet tag.

8 . A method for pallet text recognition with improved accuracy, the method comprising:
receiving a portion of an image associated with a pallet tag associated with a pallet within a retail facility from a pallet tag detection model, the image generated by an image capture device within the retail facility; recognizing a plurality of lines of text associated with the pallet tag by a text detection model; classifying the pallet tag with a classification type based on the plurality of lines of text by a classification model, the classification type of the pallet tag corresponding to a first tag format; identifying a set of qualified lines of text within the plurality of lines of text using the first tag format associated with a type of pallet tag; extracting a set of pallet attribute values from the set of qualified lines of text; assigning the extracted set of pallet attribute values to a set of pallet attribute fields within a pallet attributes table, the extracted set of pallet attribute values comprising a pallet identifier (ID); and storing the set of pallet attribute values paired with a location ID in the set of pallet attribute fields of the pallet attributes table in a data storage device, wherein the pallet ID identifies a location of the pallet within the retail facility, and wherein the location of the pallet is presented to a user via a user interface device for improved accuracy locating pallets within the retail facility.

9 . The method of claim 8 , further comprising:
classifying the pallet tag as a distribution center (DC) type of pallet tag, a store type of pallet tag or a handwritten pallet tag.

10 . The method of claim 8 , wherein the portion of the image is a cropped image, and further comprising:
detecting a tilted line of text within the plurality of lines of text within a cropped image of the portion of the pallet tag; generating a plurality of text-related values associated with a plurality of text-related pixels in the image and a plurality of non-text related values associated with a plurality of non-text related pixels in the portion of the image, the plurality of text-related values corresponding to alphanumeric characters in the tilted line of text; and enclosing the plurality of text-related values within a quadrilateral-shaped bounding box, wherein the quadrilateral-shaped bounding box encloses a plurality of lines of text detected on the pallet tag.

11 . The method of claim 8 , further comprising:
mapping the set of pallet attribute values to location data associated with a current location of a robotic device having the image capture device mounted to the robotic device, wherein the image capture device generates a set of images of the pallet for use in identifying the plurality of lines of text associated with the pallet tag on the pallet.

12 . The method of claim 8 , further comprising:
identifying a set of disqualified lines of text within the plurality of lines of text filtering the set of disqualified lines of text from the plurality of lines of text to reduce noise within the plurality of lines of text.

13 . The method of claim 8 , further comprising:
identifying an approximate location of each qualified line of text associated with a pallet attribute within the pallet tag and a type of font associated with text on the pallet tag based on the classification type.

14 . The method of claim 8 , wherein the image of the pallet tag is a first image generated at a first time, and further comprising:
receiving a second image of the pallet tag generated by the image capture device generated at a secon

Assignees

• Walmart Apollo Llc

Inventors

• Chen Yilun
• Duan Zhaoliang
• Zhang Lingfeng
• Yuan Mingquan
• Robinson William Craig
• Arora Ishan
• Ellison Benjamin
• Rader Eric W
• Siler Elizabeth Ann
• Zhang Han
• Pachaurri Abhinav
• Chakraborty Siddhartha
• Balusu Raghava
• Ghosh Ashlin
• Jade Avinash Madhusudanrao
• Anand Subhash
• Gupta Aadarsh
• Lobo Paul
• Shah Ketan
• Huang Zhiwei
• Wang Jing
• Chai Rongdong

Classifications

• G06N3/0985

  Hyperparameter optimisation; Meta-learning; Learning-to-learn · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06Q10/087 Primary

  Inventory or stock management, e.g. order filling, procurement or balancing against orders · CPC title