License Plate Detection and Character Recognition Using Deep Learning and Font Evaluation
- URL: http://arxiv.org/abs/2412.12572v1
- Date: Tue, 17 Dec 2024 06:03:42 GMT
- Title: License Plate Detection and Character Recognition Using Deep Learning and Font Evaluation
- Authors: Zahra Ebrahimi Vargoorani, Ching Yee Suen,
- Abstract summary: This paper proposes a dual deep learning strategy using a Faster R-CNN for detection and a CNN-RNN model with Connectionist Temporal Classification (CTC) loss and a MobileNet V3 backbone for recognition.
The research examines the role of font features in license plate (LP) recognition, analyzing fonts like Driver Gothic, Dreadnought, California Clarendon, and Zurich Extra Condensed with the OpenALPR system.
- Score: 0.6215404942415159
- License:
- Abstract: License plate detection (LPD) is essential for traffic management, vehicle tracking, and law enforcement but faces challenges like variable lighting and diverse font types, impacting accuracy. Traditionally reliant on image processing and machine learning, the field is now shifting towards deep learning for its robust performance in various conditions. Current methods, however, often require tailoring to specific regional datasets. This paper proposes a dual deep learning strategy using a Faster R-CNN for detection and a CNN-RNN model with Connectionist Temporal Classification (CTC) loss and a MobileNet V3 backbone for recognition. This approach aims to improve model performance using datasets from Ontario, Quebec, California, and New York State, achieving a recall rate of 92% on the Centre for Pattern Recognition and Machine Intelligence (CENPARMI) dataset and 90% on the UFPR-ALPR dataset. It includes a detailed error analysis to identify the causes of false positives. Additionally, the research examines the role of font features in license plate (LP) recognition, analyzing fonts like Driver Gothic, Dreadnought, California Clarendon, and Zurich Extra Condensed with the OpenALPR system. It discovers significant performance discrepancies influenced by font characteristics, offering insights for future LPD system enhancements. Keywords: Deep Learning, License Plate, Font Evaluation
Related papers
- Toward Enhancing Vehicle Color Recognition in Adverse Conditions: A Dataset and Benchmark [2.326743352134195]
Vehicle Color Recognition (VCR) has garnered significant research interest because color is a visually distinguishable attribute of vehicles.
Despite the success of existing methods for this task, the relatively low complexity of the datasets used in the literature has been largely overlooked.
This research addresses this gap by compiling a new dataset representing a more challenging VCR scenario.
arXiv Detail & Related papers (2024-08-21T12:54:41Z) - Deep Homography Estimation for Visual Place Recognition [49.235432979736395]
We propose a transformer-based deep homography estimation (DHE) network.
It takes the dense feature map extracted by a backbone network as input and fits homography for fast and learnable geometric verification.
Experiments on benchmark datasets show that our method can outperform several state-of-the-art methods.
arXiv Detail & Related papers (2024-02-25T13:22:17Z) - Unsupervised Domain Adaptation for Self-Driving from Past Traversal
Features [69.47588461101925]
We propose a method to adapt 3D object detectors to new driving environments.
Our approach enhances LiDAR-based detection models using spatial quantized historical features.
Experiments on real-world datasets demonstrate significant improvements.
arXiv Detail & Related papers (2023-09-21T15:00:31Z) - LoRA-like Calibration for Multimodal Deception Detection using ATSFace
Data [1.550120821358415]
We introduce an attention-aware neural network addressing challenges inherent in video data and deception dynamics.
We employ a multimodal fusion strategy that enhances accuracy; our approach yields a 92% accuracy rate on a real-life trial dataset.
arXiv Detail & Related papers (2023-09-04T06:22:25Z) - LVLane: Deep Learning for Lane Detection and Classification in
Challenging Conditions [2.5641096293146712]
We present an end-to-end lane detection and classification system based on deep learning methodologies.
In our study, we introduce a unique dataset meticulously curated to encompass scenarios that pose significant challenges for state-of-the-art (SOTA) lane localization models.
We propose a CNN-based classification branch, seamlessly integrated with the detector, facilitating the identification of distinct lane types.
arXiv Detail & Related papers (2023-07-13T16:09:53Z) - CARLA-GeAR: a Dataset Generator for a Systematic Evaluation of
Adversarial Robustness of Vision Models [61.68061613161187]
This paper presents CARLA-GeAR, a tool for the automatic generation of synthetic datasets for evaluating the robustness of neural models against physical adversarial patches.
The tool is built on the CARLA simulator, using its Python API, and allows the generation of datasets for several vision tasks in the context of autonomous driving.
The paper presents an experimental study to evaluate the performance of some defense methods against such attacks, showing how the datasets generated with CARLA-GeAR might be used in future work as a benchmark for adversarial defense in the real world.
arXiv Detail & Related papers (2022-06-09T09:17:38Z) - Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D
Object Detection [85.11649974840758]
3D object detection networks tend to be biased towards the data they are trained on.
We propose a single-frame approach for source-free, unsupervised domain adaptation of lidar-based 3D object detectors.
arXiv Detail & Related papers (2021-11-30T18:42:42Z) - Calibrating Class Activation Maps for Long-Tailed Visual Recognition [60.77124328049557]
We present two effective modifications of CNNs to improve network learning from long-tailed distribution.
First, we present a Class Activation Map (CAMC) module to improve the learning and prediction of network classifiers.
Second, we investigate the use of normalized classifiers for representation learning in long-tailed problems.
arXiv Detail & Related papers (2021-08-29T05:45:03Z) - Drifting Features: Detection and evaluation in the context of automatic
RRLs identification in VVV [0.0]
We introduce and discuss the notion of Drifting Features, related with small changes in the properties as measured in the data features.
We show that this method can efficiently identify a reduced set of features that contains useful information about the tile of origin of the sources.
arXiv Detail & Related papers (2021-05-04T19:07:32Z) - Rethinking and Designing a High-performing Automatic License Plate
Recognition Approach [16.66787965777127]
We propose a novel automatic license plate recognition (ALPR) approach, termed VSNet.
VSNet includes two CNNs, i.e., VertexNet for license plate detection and SCR-Net for license plate recognition, which is integrated in a resampling-based cascaded manner.
Experimental results show that the proposed VSNet outperforms state-of-the-art methods by more than 50% relative improvement on error rate.
arXiv Detail & Related papers (2020-11-30T16:03:57Z) - Dense Label Encoding for Boundary Discontinuity Free Rotation Detection [69.75559390700887]
This paper explores a relatively less-studied methodology based on classification.
We propose new techniques to push its frontier in two aspects.
Experiments and visual analysis on large-scale public datasets for aerial images show the effectiveness of our approach.
arXiv Detail & Related papers (2020-11-19T05:42:02Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.