CRNN_Tensorflow | Convolutional Recurrent Neural Networks(CRNN) for Scene Text Recognition | Computer Vision library

by MaybeShewill-CV Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | CRNN_Tensorflow Summary

CRNN_Tensorflow is a Python library typically used in Artificial Intelligence, Computer Vision, Deep Learning, Pytorch, Tensorflow, Neural Network applications. CRNN_Tensorflow has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can download it from GitHub.

This is a TensorFlow implementation of a Deep Neural Network for scene text recognition. It is mainly based on the paper "An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition". You can refer to the paper for architecture details. Thanks to the author Baoguang Shi. The model consists of a CNN stage extracting features which are fed to an RNN stage (Bi-LSTM) and a CTC loss.

Support

Quality

Security

License

Reuse

Support

CRNN_Tensorflow has a medium active ecosystem.

It has 1019 star(s) with 392 fork(s). There are 48 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 398 have been closed. On average issues are closed in 69 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of CRNN_Tensorflow is current.

Quality

CRNN_Tensorflow has 0 bugs and 0 code smells.

Security

CRNN_Tensorflow has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

CRNN_Tensorflow code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

CRNN_Tensorflow 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

CRNN_Tensorflow releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

CRNN_Tensorflow saves you 970 person hours of effort in developing the same functionality from scratch.

It has 2209 lines of code, 100 functions and 22 files.

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

Top functions reviewed by kandi - BETA

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

Train a Shardown dataset .
Train a ShNN dataset .
Evaluate a dataset .
Recognize a PDF image .
BatchNorm layer .
Builds a pre - trained model .
Feature extraction .
Generate training examples .
Compute accuracy .
Initialize the logger .

Get all kandi verified functions for this library.

CRNN_Tensorflow Key Features

No Key Features are available at this moment for CRNN_Tensorflow.

CRNN_Tensorflow Examples and Code Snippets

No Code Snippets are available at this moment for CRNN_Tensorflow.

Community Discussions

Trending Discussions on CRNN_Tensorflow

RHEL7 import tkinter failed inside virtualenv

How to modify the connectionist Temporal Classification (CTC) layer of the network to also give us a confidence score?

QUESTION

RHEL7 import tkinter failed inside virtualenv

Asked 2018-Sep-03 at 01:52

On Redhat 7 and python3.6

I can import tkinter without any error:

...

ANSWER

Answered 2018-Aug-31 at 17:56

Since you are using a virtual environment, you start with a "clean" Python environment with none of the system packages. This is what helps make virtual environments isolated, stable, and reproducible.

You have two choices:

Install tkinter and any other dependencies in the virtual environment with pip install.
Use the option --system-site-packages when creating the virtual environment to include system modules.

The 2nd option is easier, since you don't need to reinstall anything. However, the first option is preferred especially when you use requirements.txt and pip freeze. Then you can easily recreate that virtual environment on another system.

There are some tips that may help you in How to install Python 3, venv, virtualenv, and pipenv on RHEL

Source https://stackoverflow.com/questions/51981132

QUESTION

How to modify the connectionist Temporal Classification (CTC) layer of the network to also give us a confidence score?

Asked 2018-Aug-02 at 10:19

I am trying to recognize words from cropped images of words itself by training a CRNN(CNN+LSTM+CTC) model. I am confused how to add confidence score along with recognized words. I am uisng tensorflow and following the implementation of https://github.com/TJCVRS/CRNN_Tensorflow. Can some one suggest me how to modify the connectionist Temporal Classification (CTC) layer of the network to also give us a confidence score?

...

ANSWER

Answered 2018-Jul-20 at 13:56

One update from myself:

i finally achieved a score by, passing the predicted label back to the ctc loss function and taking the anti-log of the negative of the resulting loss. I am finding this value very accurate than taking the anti-log of log_prob.

Source https://stackoverflow.com/questions/50638015

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

Vulnerabilities

No vulnerabilities reported

Install CRNN_Tensorflow

This software has been developed on Ubuntu 16.04(x64) using python 3.5 and TensorFlow 1.12. Since it uses some recent features of TensorFlow it is incompatible with older versions.