Commodity Registration Apparatus with Image Recognition and Key Input
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Solution Overview
Problem
Conventional commodity registration systems face inefficiencies when operators attempt to register unlearnt commodities without barcodes, leading to repeated registration attempts and increased settlement times due to incorrect use of image recognition modules and subsequent reliance on PLU keys.
Innovation Solution
A commodity registration apparatus and method that combines an image capturing module, a commodity learning and storing module, and a commodity registration key input module, allowing unregistered commodities to be learned and stored through key input, enabling seamless registration of both barcoded and unbarcoded items by switching between image recognition and key input modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If the image recognition module is used to register unlearnt commodities, then the registration process appears automated, but operator determination mistakes occur leading to repeated registration attempts
Solution Approach 1:
The system provides visual feedback by displaying the captured commodity image and recognition results to the operator. This allows the operator to verify whether the commodity was correctly recognized before finalizing registration, preventing determination mistakes and reducing repeated registration attempts.
Solution Approach 2:
The image recognition module automatically captures the commodity image and performs recognition without requiring manual intervention for image capture. This semi-automated approach reduces operator burden while maintaining reliability through operator verification of the recognition results.
2Reliability
If the operator carries out commodity registration again through the PLU key after failing image recognition, then the commodity can be registered, but the waiting time during settlement becomes longer
Solution Approach 1:
The system performs preliminary image capture and recognition before the operator needs to manually input PLU codes. By preparing the recognition results in advance and displaying them to the operator, the system reduces the time required for manual registration and minimizes waiting time during settlement.
3Reliability
If the operator repeatedly carries out commodity registration through different methods, then all commodities can be registered, but work efficiency decreases
Solution Approach 1:
The system combines multiple registration methods (image recognition and PLU key input) into a single integrated interface. The operator can switch between automatic recognition and manual input methods seamlessly, ensuring all commodities can be registered while maintaining high work efficiency through a unified操作流程.
Data Source
AI summary
A commodity is learnt and stored in an HDD on the basis of a commodity image captured by an image capturing section. Commodity registration is carried out through a key input. The commodity which is not stored in the HDD yet is stored in the HDD as commodity data when the commodity registration is carried out through a key input, in this way, the registration as a learnt commodity is realized. Then the target commodity captured by the image capturing section is read from the commodity data stored in the HDD. In this way, the commodity image can be added and learnt based on that the unregistered commodity is input through a key operation by the operator.