OCR-MobilePhone-captured-form-image-recognition | form image recognition ) Form content digits
kandi X-RAY | OCR-MobilePhone-captured-form-image-recognition Summary
kandi X-RAY | OCR-MobilePhone-captured-form-image-recognition Summary
OCR-MobilePhone-captured-form-image-recognition is a Python library. OCR-MobilePhone-captured-form-image-recognition has no bugs, it has no vulnerabilities and it has low support. However OCR-MobilePhone-captured-form-image-recognition build file is not available. You can download it from GitLab.
(MobilePhone-captured-form image recognition) Form content digits recognition based on OPEN-CV and Tensorflow.
(MobilePhone-captured-form image recognition) Form content digits recognition based on OPEN-CV and Tensorflow.
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Support
OCR-MobilePhone-captured-form-image-recognition has a low active ecosystem.
It has 0 star(s) with 0 fork(s). There are no watchers for this library.
It had no major release in the last 6 months.
OCR-MobilePhone-captured-form-image-recognition has no issues reported. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of OCR-MobilePhone-captured-form-image-recognition is current.
Quality
OCR-MobilePhone-captured-form-image-recognition has no bugs reported.
Security
OCR-MobilePhone-captured-form-image-recognition has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
License
OCR-MobilePhone-captured-form-image-recognition does not have a standard license declared.
Check the repository for any license declaration and review the terms closely.
Without a license, all rights are reserved, and you cannot use the library in your applications.
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OCR-MobilePhone-captured-form-image-recognition releases are not available. You will need to build from source code and install.
OCR-MobilePhone-captured-form-image-recognition has no build file. You will be need to create the build yourself to build the component from source.
Installation instructions are not available. Examples and code snippets are available.
Top functions reviewed by kandi - BETA
kandi has reviewed OCR-MobilePhone-captured-form-image-recognition and discovered the below as its top functions. This is intended to give you an instant insight into OCR-MobilePhone-captured-form-image-recognition implemented functionality, and help decide if they suit your requirements.
- Finds the feature point in the image table
- Searches for a horizontal line
- Finds a vertical line
- returns the type of a feature_point
- Search for a grid of feature points .
- Return the cross point between two lines .
- Segmentation of image .
- Deepnn layer .
- Main function for the DeepNet .
- Runs the prediction .
Get all kandi verified functions for this library.
OCR-MobilePhone-captured-form-image-recognition Key Features
No Key Features are available at this moment for OCR-MobilePhone-captured-form-image-recognition.
OCR-MobilePhone-captured-form-image-recognition Examples and Code Snippets
No Code Snippets are available at this moment for OCR-MobilePhone-captured-form-image-recognition.
Community Discussions
No Community Discussions are available at this moment for OCR-MobilePhone-captured-form-image-recognition.Refer to stack overflow page for discussions.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install OCR-MobilePhone-captured-form-image-recognition
You can download it from GitLab.
You can use OCR-MobilePhone-captured-form-image-recognition 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.
You can use OCR-MobilePhone-captured-form-image-recognition 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 GitLab.
If you have any questions check and ask questions on community page Stack Overflow .
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