Wine Label Recognition Using OCR and Deep Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional wine management systems in cellars suffer from low accuracy in wine label recognition, leading to inefficient manual management of wines due to the limitations of conventional technologies in handling complex environments and multinational language compatibility.
Innovation Solution
A wine label recognition method combining optical character recognition (OCR) and deep learning to accurately identify wine labels, utilizing OCR for normative characters and deep learning for image recognition, narrowing the search range and reducing computational demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional OCR technology is used for wine label recognition, then the recognition process is simple, but the recognition accuracy is low in complex environments and with multinational languages
Solution Approach 1:
The patent segments the wine label recognition task into two distinct parts: OCR for extracting normative characters and deep learning for recognizing image features. This segmentation allows each method to specialize in what it does best, improving overall accuracy while managing complexity through division of labor
Solution Approach 2:
The patent merges OCR technology and deep learning technology into a unified recognition system. By combining the strengths of both methods - OCR's ability to handle normative characters and deep learning's ability to handle complex image features - the system achieves higher recognition accuracy than either method alone
2Adaptability or versatility
If deep learning recognition is used for all wine label text, then the compatibility with multinational languages improves, but the computational resources and time required increase significantly
Solution Approach 1:
The patent applies deep learning only partially - specifically for recognizing image features on wine labels - rather than using it for all text recognition. This partial application maintains the benefits of deep learning for complex cases while avoiding the excessive computational cost of applying it universally
Solution Approach 2:
The patent introduces OCR as an intermediary that handles the initial text extraction from wine labels. This intermediary filters out routine cases that don't require deep learning, allowing the system to use computational resources efficiently by reserving deep learning for more challenging recognition tasks
3Productivity
If manual management is used for wines in cellars, then the system complexity is low, but the management efficiency is relatively low
Solution Approach 1:
The patent implements an automated wine management system where the recognition system automatically identifies and manages wine labels without human intervention. The system serves itself by using the recognized label information to automatically update wine inventory and management records, eliminating the need for manual data entry and processing
Solution Approach 2:
The patent replaces the mechanical manual management process with an automated recognition-based system. Instead of manually reading and recording wine label information, the system uses OCR and deep learning to automatically capture and process label data, substituting human labor with automated technological processes
Data Source
AI summary
A wine label recognition method, a wine information management method and apparatus, a computer device, and a computer-readable storage medium are provided. The method includes: obtaining a wine image, and performing optical character recognition (OCR) on the wine image in a preset OCR manner, to obtain text included in the wine image (S21); performing deep learning recognition on the wine image in a preset deep learning recognition manner, to obtain an image feature included in the wine image (S22); and sifting out a target wine label matching the text and the image feature from a preset wine label database according to the text and the image feature, and using the target wine label as a wine label corresponding to the wine image (S33). Advantages of deep learning and OCR are fully utilized thereby improving accuracy and efficiency of wine label recognition and improving automation efficiency of wine information management.