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Found 5 papers, 1 papers with code
Benchmarking Probabilistic Deep Learning Methods for License Plate Recognition
Such an uncertainty measure allows to detect false predictions, indicating an analyst when not to trust the result of the automated license plate recognition.
Synthesizing Annotated Image and Video Data Using a Rendering-Based Pipeline for Improved License Plate Recognition
The benefits of the proposed data generation pipeline, especially for machine learning scenarios with limited available training data, are demonstrated by an extensive experimental validation in the context of automatic license plate recognition.
3D Rendering Framework for Data Augmentation in Optical Character Recognition
Applying the proposed method, improvements of up to 2. 79 percentage points in terms of Character Error Rate (CER), and up to 7. 88 percentage points in terms of Word Error Rate (WER) are achieved on the subset.
Forensic License Plate Recognition with Compression-Informed Transformers
Forensic license plate recognition (FLPR) remains an open challenge in legal contexts such as criminal investigations, where unreadable license plates (LPs) need to be deciphered from highly compressed and/or low resolution footage, e. g., from surveillance cameras.
Toward Reliable Models for Authenticating Multimedia Content: Detecting Resampling Artifacts With Bayesian Neural Networks
To this end, we propose to use Bayesian neural networks (BNN), which combine the power of deep neural networks with the rigorous probabilistic formulation of a Bayesian framework.
