Camera-Based Wine Product Positioning with OCR and Deep Learning
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Solution Overview
Problem
Conventional wine cellar management systems lack accurate and automated methods for positioning wine products, leading to inefficient manual management due to the inability to precisely locate wine products.
Innovation Solution
A wine product positioning method using a camera-based system that combines optical character recognition (OCR) and deep learning recognition to identify wine labels and determine the position of wine products within the cellar, enabling precise automatic management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual management method is used for wine products in wine cellars, then operational simplicity is maintained, but positioning accuracy and management efficiency deteriorate
Solution Approach 1:
The patent replaces manual mechanical positioning with an automated computer vision system. Cameras capture images of wine products, and image recognition algorithms automatically identify and position wine bottles based on their labels, eliminating the need for manual tracking and significantly improving positioning accuracy.
Solution Approach 2:
The system creates visual copies (images) of wine products through cameras and processes these copies to extract positioning information. By working with image data rather than physical manipulation, the system achieves accurate positioning without direct contact with the wine bottles.
2Productivity
If automated management system is introduced, then management efficiency is improved, but system complexity and cost increase
Solution Approach 1:
The patent designs a multi-functional system where the same camera and image recognition infrastructure serves multiple purposes: capturing wine product images, identifying labels, determining positions, and providing inventory management capabilities. This universal approach improves management efficiency while avoiding the need for separate specialized systems for each function.
Solution Approach 2:
The system enables wine products to be automatically identified and positioned through their own unique labels. The image recognition algorithm extracts positioning information directly from the wine label images without requiring external tags, RFID tags, or additional markers on the bottles, reducing system complexity.
3Measurement precision
If conventional image recognition is used, then implementation simplicity is maintained, but recognition accuracy deteriorates due to complex wine label patterns
Solution Approach 1:
The patent segments the wine label recognition process into distinct stages: capturing the label image, preprocessing to enhance quality, extracting text and graphical elements, and finally recognizing the wine product information. This segmented approach handles the complexity of diverse label designs systematically while maintaining implementation feasibility.
Solution Approach 2:
The system introduces an intermediary image processing stage between camera capture and final recognition. The processing module enhances label images by adjusting brightness, contrast, and sharpness, and by removing distractions like reflections or shadows, thereby improving recognition accuracy without requiring changes to the physical labels or cameras.
Data Source
AI summary
Disclosed are a wine product positioning method, a wine product information management method and apparatus, a computer device, and a computer-readable storage medium. Based on a preset camera in a wine cellar, a wine product image captured by the preset camera and corresponding to a target wine product is acquired (S21). Based on a preset wine label recognition method combining optical character recognition (OCR) and deep learning recognition, the wine product image is recognized to obtain a wine label corresponding to the wine product image (S22). A preset capture position corresponding to the camera is acquired, and the preset capture position is taken as a current position corresponding to the target wine product (S23). A position corresponding to the target wine product is described by using the wine label and the current position, to position the target wine product (S24).