Neural Network Item Recognition for Unlabeled Retail Products
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Solution Overview
Problem
Traditional retail point-of-sale systems face inefficiencies in identifying and pricing items, especially when items are not labeled with barcodes or QR codes, requiring cashiers to remember IDs or manually enter data, which can lead to errors and increased transaction times.
Innovation Solution
A machine learning system integrated with a POS system that uses a scanner and camera to identify items via barcodes, QR codes, or IDs, and employs neural networks for image classification, allowing for automated item recognition and pricing, reducing the need for manual entry and improving transaction efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional barcode or QR code labeling is used for items, then item identification can be automated, but items without labels require manual ID entry which increases transaction time and error rates
Solution Approach 1:
The system enables self-service item identification through image capture and automatic recognition. The camera captures an image of the item, the machine learning model automatically identifies the item and retrieves its ID and price, eliminating the need for manual entry by cashiers or customers.
Solution Approach 2:
The patent replaces manual mechanical entry (typing IDs into the system) with an automated optical recognition system. The camera captures visual information, and machine learning algorithms process this data to automatically identify items and retrieve pricing information.
2Ease of operation
If cashiers manually enter item IDs for unlabeled items, then item identification is possible, but human error increases and transaction speed decreases
Solution Approach 1:
The system performs self-service item identification by automatically capturing images, recognizing items through machine learning, and retrieving pricing information without human intervention, thereby eliminating human error in the identification process.
Solution Approach 2:
The system provides feedback by displaying the identified item and price to the cashier for verification. This feedback mechanism ensures accuracy while maintaining the benefits of automated identification.
3Productivity
If traditional POS systems require manual ID entry for items, then flexibility in handling unlabeled items is maintained, but operational efficiency decreases
Solution Approach 1:
The POS system is enhanced with multi-functionality by integrating image capture, machine learning-based item recognition, and automated pricing retrieval. This universal system can handle both labeled and unlabeled items, as well as various types of labeled items (barcodes, QR codes, images), through a single integrated approach.
Solution Approach 2:
The machine learning model acts as an intermediary between the camera/image input and the database query system. It processes visual information, identifies items, and translates this into actionable data for the POS system, bridging the gap between visual recognition and transaction processing.
Data Source
AI summary
A method and program product includes scanning for at least one identification means of an object. An image of the object is captured. An identification associated with the image and the image are communicated to a training system. The training system is configured for at least training a neural network system with identifications and images to produce synaptic weights. Synaptic weights are received from the training system. A predicted identification from a captured image of an object is predicted. The predicting uses at least the synaptic weights.