deep-anpr | Using neural networks to build an automatic number plate

by matthewearl Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions Vulnerabilities Install Support

kandi X-RAY | deep-anpr Summary

deep-anpr is a Python library. deep-anpr has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However deep-anpr build file is not available. You can download it from GitHub.

Using neural networks to build an automatic number plate recognition system. See this blog post for an explanation. Note: This is an experimental project and is incomplete in a number of ways, if you're looking for a practical number plate recognition system this project is not for you. If however you've read the above blog post and wish to tinker with the code, read on. If you're really keen you can tackle some of the enhancements on the Issues page to help make this project more practical. Please comment on the relevant issue if you plan on making an enhancement and we can talk through the potential solution.

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Quality

Security

License

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Support

deep-anpr has a medium active ecosystem.

It has 1804 star(s) with 715 fork(s). There are 124 watchers for this library.

It had no major release in the last 6 months.

There are 93 open issues and 26 have been closed. On average issues are closed in 154 days. There are 6 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of deep-anpr is current.

Quality

deep-anpr has 0 bugs and 0 code smells.

Security

deep-anpr has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

deep-anpr code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

deep-anpr is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

deep-anpr releases are not available. You will need to build from source code and install.

deep-anpr has no build file. You will be need to create the build yourself to build the component from source.

deep-anpr saves you 246 person hours of effort in developing the same functionality from scratch.

It has 599 lines of code, 45 functions and 7 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed deep-anpr and discovered the below as its top functions. This is intended to give you an instant insight into deep-anpr implemented functionality, and help decide if they suit your requirements.

Detects the number plate on a given image
Convolutional layers
Get the detection model
Generate scaled image
Softmax function
Bias Variable
2d convolutional layer
Max pooling op
Creates a weight variable
Sigmoid function
Perform post - processing
Group overlapping rectangles
Checks if two bounding boxes overlap
Get training model
Convert a sequence of letter probabilities to code

Get all kandi verified functions for this library.

deep-anpr Key Features

No Key Features are available at this moment for deep-anpr.

deep-anpr Examples and Code Snippets

No Code Snippets are available at this moment for deep-anpr.

Community Discussions

No Community Discussions are available at this moment for deep-anpr.Refer to stack overflow page for discussions.

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install deep-anpr

You can download it from GitHub.
You can use deep-anpr like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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