# tf-crnn

**Repository Path**: moonharbour/tf-crnn

## Basic Information

- **Project Name**: tf-crnn
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: GPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-04-20
- **Last Updated**: 2021-11-03

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Text recognition with Convolutional Recurrent Neural Network and TensorFlow 2.0 (tf2-crnn)

[![Documentation Status](https://readthedocs.org/projects/tf-crnn/badge/?version=latest)](https://tf-crnn.readthedocs.io/en/latest/?badge=latest)

Implementation of a Convolutional Recurrent Neural Network (CRNN) for image-based sequence recognition tasks, such as scene text recognition and OCR. 

This implementation is based on Tensorflow 2.0 and uses `tf.keras` and `tf.data` modules to build the model and to handle input data.

To access the previous version implementing Shi et al. paper, go to the [v.0.5.2](https://github.com/solivr/tf-crnn/tree/v0.5.2) tag.


## Installation
`tf_crnn` makes use of `tensorflow-gpu` package (so CUDA and cuDNN are needed). 

You can install it using the `environment.yml` file provided and use it within an environment.
    
    conda env create -f environment.yml

See also the [docs](https://tf-crnn.readthedocs.io/en/latest/start/index.html#) for more information.


## Try it
 
 Train a model with [IAM dataset](http://www.fki.inf.unibe.ch/databases/iam-handwriting-database).
 
 **Create an account**
 
 Create an account on the official IAM dataset page in order to access the data.
 Export your credentials as enviornment variables, they will be used by the download script.
 
    export IAM_USER=<your-username>
    export IAM_PWD=<your-password>
 
 
 **Generate the data in the correct format**

    cd hlp
    python prepare_iam.py --download_dir ../data/iam --generated_data_dir ../data/iam/generated
    cd ..
    
**Train the model**

    python training.py with config.json

More details in the [documentation](https://tf-crnn.readthedocs.io/en/latest/start/training.html#example-of-training).