Container Identification Code Recognition Using Image Merging
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Solution Overview
Problem
Existing systems for automatically recognizing identification codes from digital images, such as cargo container codes and vehicle license plates, face challenges due to factors like movement and varying lighting conditions, leading to reduced recognition accuracy.
Innovation Solution
A method and system that combine multiple digital images to extract character sequences, determine the longest common subsequence, and select the identification code candidate with the highest average confidence score, using a camera system and electronic circuitry to improve recognition performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single digital image is used for identification code recognition, then the system is simple and fast, but recognition accuracy deteriorates due to dirt, movement, or lighting conditions
Solution Approach 1:
The patent combines multiple digital images captured from the same object to improve recognition accuracy. The system extracts character sequences from multiple images and merges them by identifying common segments and concatenating them to form a more reliable identification code, thereby resolving the contradiction between accuracy and complexity
Solution Approach 2:
The system performs preliminary actions by capturing multiple images before final recognition. It extracts and processes character sequences from these pre-captured images, identifying common segments and constructing candidate codes that can be verified against the original images, thus improving accuracy without requiring complex real-time processing
2Measurement precision
If multiple digital images are combined to improve recognition accuracy, then recognition accuracy improves, but processing time and computational complexity increase
Solution Approach 1:
The patent extracts only the necessary character sequences from multiple images and focuses processing on identifying common segments between these sequences. By extracting and filtering out redundant information, the system reduces computational complexity while maintaining improved recognition accuracy
Solution Approach 2:
The system performs partial processing by extracting character sequences and identifying common segments rather than processing every pixel and detail from all images. This partial action approach maintains sufficient accuracy while significantly reducing processing time and computational resources required
3Reliability
If character sequences are combined from multiple images, then recognition reliability improves, but the complexity of combining and validating sequences increases
Solution Approach 1:
The patent segments the recognition process into distinct steps: extracting character sequences from individual images, identifying common segments between sequences, concatenating these segments to form candidate codes, and validating against original images. This segmentation reduces overall complexity by breaking down the complex task into manageable, sequential operations
Solution Approach 2:
The system incorporates feedback by validating the constructed candidate codes against the original digital images. This feedback mechanism ensures reliability by checking whether the combined character sequences accurately represent the identification code in the source images, allowing for correction and verification without requiring overly complex processing
Data Source
AI summary
A method and system for container identification are disclosed. The method comprises obtaining a plurality of digital images of a character sequence on the container, extracting the character sequences from the images, combining the character sequences into at least one identification code candidate, and selecting one of the candidates as the identification code. The system comprises at least one camera and a computer system that is electrically coupled to the camera, whereby when the computer system receives a trigger signal, said computer system takes a plurality of digital images from the camera, produces character sequences as partial recognition results for the plurality of digital images, and combines the partial recognition results together to produce the identification code.