CRNN | A TensorFlow implementation of https://githubcom/bgshih/crnn | Computer Vision library

by Belval Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | CRNN Summary

CRNN is a Python library typically used in Artificial Intelligence, Computer Vision, Tensorflow applications. CRNN has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

It is a Convolutional Recurrent Neural Network that can be used as an OCR.

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Support

CRNN has a low active ecosystem.

It has 298 star(s) with 101 fork(s). There are 14 watchers for this library.

It had no major release in the last 6 months.

There are 30 open issues and 30 have been closed. On average issues are closed in 73 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of CRNN is current.

Quality

CRNN has 0 bugs and 0 code smells.

Security

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

CRNN code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

CRNN 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 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.

CRNN saves you 237 person hours of effort in developing the same functionality from scratch.

It has 579 lines of code, 20 functions and 5 files.

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

Top functions reviewed by kandi - BETA

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

Create batch generator
Create a sparse matrix from a sequence of sequences
Resize an image
Convert a label to an array
Train the model
Saves the frozen model to disk
Convert groundtruth to a word
Argument parser
Generate all test batches
Generate all train batches
Loads example data
Runs the test

Get all kandi verified functions for this library.

CRNN Key Features

No Key Features are available at this moment for CRNN.

CRNN Examples and Code Snippets

No Code Snippets are available at this moment for CRNN.

Community Discussions

Trending Discussions on CRNN

text recognition and restructuring OCR opencv

How to monitor accuracy with CTC loss function and Datasets? (runnable code included)

How to run Neural Network model on Android taking advantage of GPU?

ValueError: expected ndim=3, found ndim=2 after replacing BatchNormalization

Deep learning Keras model CTC_Loss gives loss = infinity

Is it possible to use the output of a flatten layer of a CNN to be the input of a RNN?

Why casting input and model to float16 doesn't work?

String to nested Object parser

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

How do i add ctc beam search decoder in crnn model (pytorch)

QUESTION

text recognition and restructuring OCR opencv

Asked 2021-Jun-08 at 12:14

Link to original image https://ibb.co/0VC6vkX

I am currently working with an OCR Project. I pre-processed the image, and then applied pre-trained EAST model for text detection.

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ANSWER

Answered 2021-Jun-07 at 07:02

Here's a possible solution that you can try improving on by trying a few things:

by varying Gaussian parameters
by thresholding the blurred image to see if it improves the result

Code:

Source https://stackoverflow.com/questions/67763853

QUESTION

How to monitor accuracy with CTC loss function and Datasets? (runnable code included)

Asked 2021-May-19 at 20:37

I've been trying to speed up training of my CRNN network for optical character recognition, but I can't get the accuracy metric working when using TFRecords and tf.data.Dataset pipelines. I previously used a Keras Sequence and had it working. Here is a complete runnable toy example showing my problem (tested with Tensorflow 2.4.1):

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ANSWER

Answered 2021-May-17 at 09:45

There probably some issue with [accuracy] with tf.data, but I'm not super sure if this is the main cause in your case or if the issue still exits. If I try as follows, it runs anyway without Sequence (with tf.data).

Source https://stackoverflow.com/questions/67506106

QUESTION

How to run Neural Network model on Android taking advantage of GPU?

Asked 2020-May-20 at 11:08

Anyone tried to run object detection or crnn model on Android? I tried to run crnn model (serialized pytorch) but it takes 1s on Huawei P30 lite and 5s on Samsung J4 Core.

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ANSWER

Answered 2020-Apr-24 at 11:41

At the moment it is not possible to run pytorch on am ARM-GPU:

Github Issue

PyTorch Forum

I think the differences in speed result out of the differnten cpu's!

Source https://stackoverflow.com/questions/61407421

QUESTION

ValueError: expected ndim=3, found ndim=2 after replacing BatchNormalization

Asked 2019-Nov-05 at 16:13

I'm programming in python 3.7.5 using keras and TensorFlow 1.13.1

I want remove batch normalization layer from model coded below:

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ANSWER

Answered 2019-Nov-05 at 16:13

As per the error code, LSTM layers expect 3D input tensors, but Dense outputs only 2D. Many possible fixes exist, but not all will work equally well:

Conv2D outputs 4D tensors, shaped (samples, height, width, channels)
LSTM expects input shaped (samples, timesteps, channels)
Thus, you need to somehow transform the (height, width) dimensions into timesteps

In existing research, image data is flattened and treated sequentially - however, channels remain untouched. Thus, a viable approach is to use Reshape to yield a 3D tensor shaped (samples, height*width, channels). Finally, as Dense cannot work with 3D data, you'll need the TimeDistributed wrapper that'll apply the same Dense weights to dim 1 of input - i.e. to timesteps:

Source https://stackoverflow.com/questions/58714685

QUESTION

Deep learning Keras model CTC_Loss gives loss = infinity

Asked 2019-Sep-19 at 09:19

i've a CRNN model for text recognition, it was published on Github, trained on english language,

Now i'm doing the same thing using this algorithm but for arabic.

My ctc function is:

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ANSWER

Answered 2019-May-31 at 19:49

This error is happened when image text have two equal characters in the same sequence e.g happen --> pp. for so that you can remove data that has this characteristic.

Source https://stackoverflow.com/questions/52283000

QUESTION

Is it possible to use the output of a flatten layer of a CNN to be the input of a RNN?

Asked 2019-Sep-02 at 20:03

I'm currently doing my honours research project on online/dynamic signature verification. I am using the SVC 2004 dataset (Task 2). The aim of my research is to create a CRNN (convolutional recurrent neural network) that can identify if a signature is authentic or forged. Here is the code for the model: (my data preprocessing can be found here: Data preprocessing code

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ANSWER

Answered 2019-Sep-02 at 20:03

Flatten converts a tensor of shape (batch_size, timesteps, features) to (batch_size, timesteps*features) which is why you are getting the error found ndim=2. Depending on what you are trying to achieve you might:

Remove Flatten to pass the convolved learned features into an LSTM, or
Reshape the flattened tensor to (batch_size, timesteps*features, 1) essentially saying every timestep is a single feature.

In either case the LSTM expects a tensor of rank 3. But wait, just because you reshape doesn't mean it is correct, it all depends on what you are trying to achieve and how the information flow / computation graph of the network should look like.

Source https://stackoverflow.com/questions/57761323

QUESTION

Why casting input and model to float16 doesn't work?

Asked 2019-Jul-28 at 11:35

I'm trying to change inputs and a deep learning model to flaot16, since I'm using T4 GPU and they work much faster with fp16. Here's part of the code: I first have my model and then made some dummy data point for the sake of figuring the data casting figured out first (I ran it with the whole batch and got the same error).

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ANSWER

Answered 2019-Jul-28 at 05:32

Check out your implementation of CRNN. My guess is that you have "hidden" state tensor stored in the model, but not as a "buffer" but just as a regular tensor. Therefore, when casting the model to float16 the hidden state remains float32 and causes you this error.

Try to store the hidden state as a register in the module (see register_buffer for more info).
Alternatively, you can explicitly cast to float16 any member tensor in the module by overloading the .to() method of your model.

Source https://stackoverflow.com/questions/57236261

QUESTION

String to nested Object parser

Asked 2019-Feb-11 at 08:27

I am writing a graphical interface program to solve chemical equations and to be other functions such as displaying a periodic table in a different window.

As the title suggests I'm having issues with the string to objects parser and would like some help with getting it to work.

It gets a String formula (C6H14 + O2 => CO2 + H2O) and will divide it up into different objects (Molecule(s) has a list of Element(s) which store the name and quantity), which then is returned. Note* I have it set as void for testing purposes only.

I have split the functions into two parts: The first part reads the String into Element objects; a name and a quantity. The second part makes the Molecule objects and checks where one molecule starts and another one ends and places the element object accordingly. This is why I do not filter away the numerical actions such as +, = and >.

Parser:

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ANSWER

Answered 2019-Feb-11 at 08:27

I solved it by using a temporary Molecule object variable which then are inserted into the list.

Source https://stackoverflow.com/questions/54280143

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?

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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

QUESTION

How do i add ctc beam search decoder in crnn model (pytorch)

Asked 2018-Jul-20 at 12:37

I am following the CRNN implementation of https://github.com/meijieru/crnn.pytorch, but seems like it is not using beam search for decoding the words. Can someone tell me how to add beam search decoding in the same model? At the same time in Tensorflow, there is an inbuilt tf.nn.ctc_beam_search_decoder.

...

ANSWER

Answered 2018-Jul-20 at 12:17

i know its not a great idea, but i did it using tensorflow inside pytorch.

Source https://stackoverflow.com/questions/51422776

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

Vulnerabilities

No vulnerabilities reported

Install CRNN

You can download it from GitHub.
You can use CRNN 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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