License Plate OCR Correction via Hash Database
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Solution Overview
Problem
Existing technologies for recognizing vehicle license plates using optical character recognition (OCR) often suffer from errors, especially under unfavorable lighting conditions, which can lead to incorrect identification and potential misuse, such as toll evasion.
Innovation Solution
A system and method that utilize a hash function to generate a hash value from the OCR-recognized license plate, which is then compared to a correction database containing hash values and corresponding correction instructions. This allows for automatic correction of OCR errors without storing the actual license plate, thus maintaining data protection compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If OCR is used to recognize license plates, then automated identification is achieved, but recognition errors occur under unfavorable lighting conditions
Solution Approach 1:
The system performs preliminary actions by capturing multiple images of the license plate at different locations and times before final recognition. These pre-captured images are stored and can be used to correct OCR errors if the initial recognition fails, thereby improving reliability while maintaining automation.
Solution Approach 2:
The system implements feedback mechanisms where OCR recognition results are verified against stored images and previous recognition data. If errors are detected, the system uses feedback information to correct the recognition result, improving accuracy without requiring manual intervention and thus maintaining automated operation.
2Reliability
If correction databases store actual license plates for error correction, then recognition accuracy improves, but data protection compliance is compromised
Solution Approach 1:
Instead of storing actual license plate data in the correction database, the system creates and stores only hash values (digital fingerprints) of the license plates. These hash values are used for comparison and error correction while the actual license plate data remains protected and is not stored, thus maintaining both correction capability and data protection compliance.
Solution Approach 2:
The system introduces hash values as an intermediary between the license plate data and the correction database. The hash values serve as mediators that enable error correction through comparison while preventing direct storage of sensitive license plate information, thus resolving the contradiction between correction accuracy and data protection.
3Reliability
If multiple OCR readings are performed to improve accuracy, then error rate decreases, but processing time increases
Solution Approach 1:
The system performs preliminary OCR readings and stores the results along with confidence measures in a database. When a new license plate image is processed, the system quickly compares the new reading against pre-stored results using hash values, allowing for rapid error correction without requiring multiple sequential OCR readings, thus improving accuracy while minimizing processing time.
Data Source
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AI summary
A computer-implemented method and a system for correcting the license plate of a motor vehicle are disclosed.The procedure comprises the following steps: a) capturing (10) a license plate, b) generating (20) an OCR license plate from the captured license plate using text recognition, c) performing a reference test for the generated OCR license plate, the following steps being carried out only if the reference test is negative, d) forming (40) a hash value from the generated OCR license plate, e) comparing (50) the formed hash value with a correction database, the correction database comprising stored hash values of license plates and correction instructions, each stored hash value being associated with at least one correction instruction, and f) if the formed hash value corresponds to a hash value stored in the correction database, applying (60) the at least one correction instruction associated with the stored hash value to the OCR license plate and generating a corrected OCR license plate.