Segmented Label Recognition for Illumination and Orientation Variation
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Solution Overview
Problem
Current label recognition systems are susceptible to variations in illumination, product orientation, and damage, leading to inaccurate and computationally expensive character recognition, especially in uncontrolled industrial environments.
Innovation Solution
A system utilizing a combination of label, word, and character-level recognition mechanisms, incorporating machine learning and deep learning-based trainable modules to segment and recognize objects of interest, with adaptable error tolerances and confidence scoring for accurate label identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If character recognition-based methods are used, then sensitivity to light, scale, and orientation is reduced, but computational cost increases significantly
Solution Approach 1:
The system segments the label recognition task into multiple levels: label-level recognition, word-level recognition, and character-level recognition. This segmentation allows the system to perform coarse-grained recognition first (label and word levels) which are computationally less expensive, and only perform detailed character-level analysis when necessary, thereby reducing overall computational cost while maintaining high accuracy.
Solution Approach 2:
The system applies partial character recognition only to specific regions or characters that require detailed analysis, rather than performing complete character recognition on the entire label. This selective approach reduces computational expenditure while maintaining sufficient accuracy for verification purposes.
2Productivity
If template matching methods are used, then processing speed is improved, but sensitivity to illumination variations, camera distance, and product orientation increases
Solution Approach 1:
The system dynamically adjusts the recognition strategy based on the input image characteristics. When illumination, orientation, or scale variations are detected, the system automatically switches from template matching to character recognition methods, and vice versa. This dynamic adaptation allows the system to maintain both speed and accuracy by selecting the most appropriate method for each specific condition.
Solution Approach 2:
The system changes the recognition parameters and methods based on detected conditions such as illumination level, image quality, and orientation. By monitoring these parameters and adjusting the recognition approach accordingly, the system maintains high accuracy across varying conditions while preserving processing efficiency.
3Adaptability or versatility
If character recognition systems are used, then adaptability to environmental factors is improved, but accuracy with damaged text or labels decreases
Solution Approach 1:
The system performs preliminary label and word level recognition before character-level analysis. This preliminary action allows the system to identify the overall label structure and context, which then guides the character-level recognition process. When damage is detected in certain areas, the system can focus character analysis only on critical regions, improving overall accuracy while maintaining adaptability to various environmental conditions.
Data Source
AI summary
Methods and systems for recognizing one or more labels are disclosed. The method includes receiving at least one image, wherein the at least one image includes one or more objects. The method also includes processing the received at least one image to detect the one or more objects and displaying the one or more labels in the received at least one image using the detected one or more objects.