Logo Recognition System Using Modular Detection Modules
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Solution Overview
Problem
Current logo recognition systems in images are either time-consuming, inaccurate, or not scalable for real-time applications, particularly failing when dealing with symbols, typographic content, or variations of existing logos, and require continuous re-training of datasets for new logos.
Innovation Solution
A system comprising an image processing module and a logo database with multiple recognition modules (text-based, image-based, sign-based, and region-based) that preprocess images, detect and recognize logos using optical character recognition, local and global feature extraction, object detection, and segmentation techniques, allowing for accurate recognition of text, symbol, and combination logos, including variants, and updating the database in real time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If object detection techniques are used to quickly detect and recognize known logos, then recognition speed is improved, but scalability is lost
Solution Approach 1:
The system segments the logo recognition task into multiple specialized modules: text-based logo recognition module for logos containing text, image-based logo recognition module for symbol-based logos, and combination modules for mixed types. Each module handles specific logo types independently, enabling the system to scale by adding or activating modules as needed without retraining a single monolithic model.
2Measurement precision
If neural networks and artificial intelligence techniques are employed to provide accurate results, then recognition accuracy is improved, but computational time increases
Solution Approach 1:
The system dynamically selects which recognition module to execute based on the input image characteristics and logo type. The logo recognition controller determines whether to use text-based recognition, image-based recognition, or combination approaches, activating only the necessary computational resources for each specific case rather than running all neural network modules uniformly.
Solution Approach 2:
The system applies partial action by using simpler recognition methods (text comparison, edge detection) when sufficient for the task, and only invoking more computationally intensive neural network-based image recognition when necessary. This selective application of computational complexity maintains accuracy while reducing overall processing time.
3Device complexity
If a single recognition approach is used to handle all logo types, then system complexity is reduced, but accuracy decreases for specific logo types like symbols and typographic content
Solution Approach 1:
Each recognition module is optimized for specific local characteristics of logos: the text-based module specializes in typographic content with OCR and text comparison, the image-based module specializes in symbol-based logos with feature extraction and matching, and the combination module handles mixed types. This local specialization ensures high accuracy for each logo type without requiring a single complex universal solution.
4Device complexity
If existing logo databases are used without updates, then system simplicity is maintained, but the system cannot recognize variants of existing logos
Solution Approach 1:
The system incorporates feedback mechanisms where recognized logos and their variants are fed back into the logo database for future recognition. The logo database is dynamically updated with newly recognized logos and their variations, enabling the system to learn and adapt to new logo types and variants while maintaining the existing database structure and simplicity.
Data Source
AI summary
A system, a method, and a computer program product for recognizing a logo from an image is provided. The method comprises receiving and preprocessing an image that contains the logo to be recognized. The method further comprises detecting text of the logo to recognize the logo. The method furthermore comprises extracting local and global features of a region of interest associated with the logo to recognize the logo if the text is not detected in the logo. The method further comprises detecting one or more signs in proximity of the logo to recognize the logo if the logo is not recognized by extracting the local and global features of the logo. The method finally comprises identifying a region where the logo is present in the pre-processed image to recognize the logo if the logo is not recognized by detecting the one or more signs in proximity of the logo.