https://github.com/jedrzejboczar/tf-character-recognition

Character recognition from images using Tensorflow
https://github.com/jedrzejboczar/tf-character-recognition

Last synced: about 2 months ago
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Character recognition from images using Tensorflow

• Host: GitHub
• URL: https://github.com/jedrzejboczar/tf-character-recognition
• Owner: jedrzejboczar
• License: mit
• Created: 2019-09-10T11:36:30.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2021-05-21T15:50:25.000Z (over 3 years ago)
• Last Synced: 2023-03-05T12:05:29.297Z (almost 2 years ago)
• Language: Python
• Size: 285 KB
• Stars: 2
• Watchers: 2
• Forks: 1
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# tf-character-recognition

This project is an exploration of Tensorflow library (v1). 

It has been used to train Convolutional Neural Networks (CNNs) to recognise characters (digits, lower- and uppercase letters)

from images. 

As for the database, the [Chars74K](http://www.ee.surrey.ac.uk/CVSSP/demos/chars74k/) dataset has been used.

It contains 3 types of images: natural photos, handwritten characters (on a tablet) and images synthesised from fonts.

To download the dataset use the script at `database/chars74k/prepare_database.py`. 

This will download all the necessary files, extract them and perform the initial preperation of image files for future use.

## Current state

I've written this some time ago. Since then tensoflow had some updates, along with version v2. 

The requiremets.txt file has been generated with `pipreqs` after some time 

and it doesn't reflect the state of version from development time,

so there are multiple deprecation warnings and some things do not work at all.

This is a  big TODO, but at the moment I don't have time to fix it.

## Project organisation

```

.

├── cnn_model.py     # definitions of tensorflow models

├── cv2_show.py      # some utilities for viewing images dataset

├── database/        # all the datasets used and their loaders

├── data.py          # generalization over datasets and utilities for image destortions, etc.

├── gui.py           # PyQt GUI for testing recognition on hand written images

├── log.py           # logging

├── models/          # here tensorflow models are stored (this is gitignored)

└── run.py           # main launcher

```

The main script of interest is `run.py` (but other scripts may also provide mains for some special usage).

The models are stored in `models/` directory, but it is ignored, so first a model has to be trained before anything can be done.

Some pretrained and quite well-working models can be found in releases to avoid the time consuming training process.

## Examples 

See help:

```

python run.py -h

```

Probably the most interesting thing that is also straghtforward to use is the GUI:

```

python run.py -G

```

Some screenshots:















Another quite straightforward option is the visualization of a walk in latent space:

```

python run.p -DW

```

Download this ![video](media/latent-space-walk.mp4) for an example result of latent space walk.