Alpr Unconstrained

License Plate Detection and Recognition in Unconstrained Scenarios
Alternatives To Alpr Unconstrained
Project NameStarsDownloadsRepos Using ThisPackages Using ThisMost Recent CommitTotal ReleasesLatest ReleaseOpen IssuesLicenseLanguage
Face Api.js14,578
3 months ago414mitTypeScript
JavaScript API for face detection and face recognition in the browser and nodejs with tensorflow.js
a month ago1October 23, 202094gpl-3.0Python
A Deep-Learning-Based Chinese Speech Recognition System 基于深度学习的中文语音识别系统
Lstm Human Activity Recognition3,074
5 months ago19mitJupyter Notebook
Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier
a year ago69mitPython
End-to-end Automatic Speech Recognition for Madarian and English in Tensorflow
Tensorflow Speech Recognition2,124
5 months ago32otherPython
🎙Speech recognition using the tensorflow deep learning framework, sequence-to-sequence neural networks
Zh Ner Tf1,761
3 years ago67Python
A very simple BiLSTM-CRF model for Chinese Named Entity Recognition 中文命名实体识别 (TensorFlow)
4 years ago15apache-2.0Python
Named Entity Recognition (LSTM + CRF) - Tensorflow
6 days ago40apache-2.0JavaScript
Labelbox is the fastest way to annotate data to build and ship computer vision applications.
Alpr Unconstrained1,462
a year ago106otherC
License Plate Detection and Recognition in Unconstrained Scenarios
Lip Reading Deeplearning1,433
3 years ago1apache-2.0Python
:unlock: Lip Reading - Cross Audio-Visual Recognition using 3D Architectures
Alternatives To Alpr Unconstrained
Select To Compare

Alternative Project Comparisons

ALPR in Unscontrained Scenarios


This repository contains the author's implementation of ECCV 2018 paper "License Plate Detection and Recognition in Unconstrained Scenarios".

If you use results produced by our code in any publication, please cite our paper:

  author={S. M. Silva and C. R. Jung}, 
  booktitle={2018 European Conference on Computer Vision (ECCV)}, 
  title={License Plate Detection and Recognition in Unconstrained Scenarios}, 


In order to easily run the code, you must have installed the Keras framework with TensorFlow backend. The Darknet framework is self-contained in the "darknet" folder and must be compiled before running the tests. To build Darknet just type "make" in "darknet" folder:

$ cd darknet && make

The current version was tested in an Ubuntu 16.04 machine, with Keras 2.2.4, TensorFlow 1.5.0, OpenCV 2.4.9, NumPy 1.14 and Python 2.7.

Download Models

After building the Darknet framework, you must execute the "" script. This will download all the trained models:

$ bash

Running a simple test

Use the script "" to run our ALPR approach. It requires 3 arguments:

  • Input directory (-i): should contain at least 1 image in JPG or PNG format;
  • Output directory (-o): during the recognition process, many temporary files will be generated inside this directory and erased in the end. The remaining files will be related to the automatic annotated image;
  • CSV file (-c): specify an output CSV file.
$ bash && bash -i samples/test -o /tmp/output -c /tmp/output/results.csv

Training the LP detector

To train the LP detector network from scratch, or fine-tuning it for new samples, you can use the script. In folder samples/train-detector there are 3 annotated samples which are used just for demonstration purposes. To correctly reproduce our experiments, this folder must be filled with all the annotations provided in the training set, and their respective images transferred from the original datasets.

The following command can be used to train the network from scratch considering the data inside the train-detector folder:

$ mkdir models
$ python eccv models/eccv-model-scracth
$ python --model models/eccv-model-scracth --name my-trained-model --train-dir samples/train-detector --output-dir models/my-trained-model/ -op Adam -lr .001 -its 300000 -bs 64

For fine-tunning, use your model with --model option.

A word on GPU and CPU

We know that not everyone has an NVIDIA card available, and sometimes it is cumbersome to properly configure CUDA. Thus, we opted to set the Darknet makefile to use CPU as default instead of GPU to favor an easy execution for most people instead of a fast performance. Therefore, the vehicle detection and OCR will be pretty slow. If you want to accelerate them, please edit the Darknet makefile variables to use GPU.

Popular Recognition Projects
Popular Tensorflow Projects
Popular Machine Learning Categories
Related Searches

Get A Weekly Email With Trending Projects For These Categories
No Spam. Unsubscribe easily at any time.