USDOT Number Recognition Using Prior Probability Validation
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Solution Overview
Problem
Current automated camera-based systems for recognizing USDOT numbers on commercial vehicles face challenges due to variations in font, size, noise, low contrast, and illumination, leading to reduced recognition accuracy.
Innovation Solution
The proposed solution leverages prior information such as a pool of valid USDOT numbers and their appearance probabilities from the US Department of Transportation website to improve recognition accuracy by using OCR methods, beam search algorithms, and image processing techniques to localize and validate candidate USDOT numbers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automated camera-based recognition is used to identify USDOT numbers, then recognition speed and automation are improved, but recognition accuracy deteriorates due to variations in font, size, noise, low contrast, and illumination
Solution Approach 1:
The system performs preliminary actions by localizing the USDOT tag number in the image before performing OCR recognition. This preliminary localization step prepares the data by identifying the specific region of interest, which improves the accuracy of subsequent recognition by focusing processing on the relevant area rather than the entire image.
Solution Approach 2:
The system introduces an intermediary validation step that checks whether the recognized USDOT number exists in the official FMCSA database. This intermediary verification acts as a mediator between the OCR recognition and final output, filtering out incorrect recognitions by comparing against authoritative source data, thereby improving overall accuracy.
2Loss of time
If OCR is performed directly on captured images without preprocessing, then processing time is reduced, but recognition accuracy deteriorates due to noise and low contrast
Solution Approach 1:
The system segments the image processing into distinct stages: first localizing the USDOT tag number to identify its position and boundaries, then performing OCR only on the localized region. This segmentation allows for focused processing that improves accuracy while managing time efficiency by avoiding unnecessary processing of entire images.
Solution Approach 2:
The system performs preliminary localization of the USDOT tag number before conducting OCR recognition. This preliminary action prepares the data by identifying and isolating the relevant text region, which improves subsequent recognition accuracy by reducing the impact of noise and low contrast in other parts of the image.
3Adaptability or versatility
If a robust algorithm is designed to handle all variations in USDOT number displays, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system uses the FMCSA database as an intermediary to validate recognized USDOT numbers. This intermediary approach simplifies the algorithm by offloading the complexity of handling all possible variations to the authoritative database, where the system only needs to check whether recognized numbers exist in the database rather than implementing complex rules to handle every variation.
Solution Approach 2:
The system leverages the self-service capability of the FMCSA database to verify USDOT number validity. Instead of implementing complex validation logic within the recognition algorithm, the system allows the database to perform the verification service, thereby reducing algorithmic complexity while maintaining high adaptability to various display formats.
Data Source
AI summary
Methods and systems for tag recognition in captured images. A candidate region can be localized from regions of interest with respect to a tag and a tag number shown in the regions of interest within a side image of a vehicle. A number of confidence levels can then be calculated with respect to each digit recognized as a result of an optical character recognition operation performed with respect to the tag number. Optimal candidates within the candidate region can be determined for the tag number based on individual character confidence levels among the confidence levels. Optimal candidates from a pool of valid tag numbers can then be validated using prior appearance probabilities and data returned, which is indicative of the most probable tag to be detected to improve image recognition accuracy.