Pallet Tag Classification for Accurate OCR in Retail Facilities
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Solution Overview
Problem
Existing systems struggle with accurately identifying and locating pallets in retail facilities due to sub-optimal image data such as gray scale images, tilted text, and varying pallet tag formats, leading to unreliable and labor-intensive manual corrections.
Innovation Solution
A system utilizing a convolutional neural network (CRNN) model for pallet tag classification and text recognition, capable of handling different tag formats and edge cases, to automatically extract pallet attributes and improve identification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If computer vision is used to automatically analyze pallet images, then productivity is improved, but measurement precision deteriorates due to sub-optimal image data
Solution Approach 1:
The system segments the text recognition process by first detecting text regions in pallet tag images, then classifying each detected text line according to predefined tag formats. This segmentation allows the system to handle sub-optimal images more effectively by processing text detection and format classification as separate stages, thereby maintaining productivity while improving measurement precision through targeted text analysis.
Solution Approach 2:
The system changes parameters by classifying pallet tags according to different formats (e.g., standard tags, shipping labels, handwritten tags) and applying format-specific recognition rules. This parameter change approach enables the system to adapt to varying image qualities and text orientations, resolving the contradiction between automated processing speed and recognition accuracy by adjusting recognition parameters based on detected tag formats.
2Measurement precision
If manual verification is used to correct pallet identification errors, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system implements self-service by automatically classifying pallet tag formats and selecting appropriate recognition methods without human intervention. The computer vision system performs self-verification by cross-checking detected text against known tag format patterns, thereby maintaining high measurement precision while eliminating the time loss associated with manual verification of pallet identification and location data.
3Device complexity
If traditional text recognition is used on varied pallet tag formats, then device complexity is reduced, but reliability deteriorates
Solution Approach 1:
The system achieves universality by implementing a multi-functional classification module that can identify and process multiple pallet tag formats (standard tags, shipping labels, handwritten tags) using a single integrated system. This universal approach maintains device complexity at acceptable levels while significantly improving reliability by adapting recognition methods to match the specific format of each detected pallet tag, rather than requiring separate systems for each format type.
Data Source
AI summary
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.