https://github.com/tmcclintock/streetviewnumbers

Using AI to recognize address numbers.
https://github.com/tmcclintock/streetviewnumbers

Last synced: 14 days ago
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Using AI to recognize address numbers.

• Host: GitHub
• URL: https://github.com/tmcclintock/streetviewnumbers
• Owner: tmcclintock
• License: mit
• Created: 2020-02-13T10:43:06.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2020-02-13T22:54:00.000Z (almost 5 years ago)
• Last Synced: 2024-12-14T17:09:35.673Z (about 1 month ago)
• Language: Jupyter Notebook
• Size: 91.8 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# StreetViewNumbers [![Build Status](https://travis-ci.com/tmcclintock/StreetViewNumbers.svg?branch=master)](https://travis-ci.com/tmcclintock/StreetViewNumbers) [![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)

Using AI to recognize address numbers.

## Insight data challenge

This repository is part of the Insight Data Challenge series, which has well-defined data science challenges that I am supposed to complete in less than six hours. For this project, I am using the [street view house numbers dataset](http://ufldl.stanford.edu/housenumbers/) (SVHN). For now, I will only be using the cropped, 32x32 centered-on-a-digit format (format 2 on the website).

## Installation

First install the requirements and then this project with

```bash

pip install -r requirements.txt

python setup.py install

```

The requirements are:

* [`numpy` and `scipy`](https://scipy.org/install.html)

* [`scikit-learn`](https://scikit-learn.org/stable/install.html)

* [`tensorflow`](https://www.tensorflow.org/install) >= version 2.0.0

* [`notebook`](https://jupyter.readthedocs.io/en/latest/install.html) (for running the example notebooks)

* [`matplotlib`](https://matplotlib.org/users/installing.html) (for notebooks)

* [`pytest`](https://docs.pytest.org/en/latest/getting-started.html) (for testing)

These can all be installed together using the `requirements.txt` file as shown above (assuming you have `pip`).

Once installed, test your installation with

```bash

pytest

```

If any tests fail, please raise an issue [here](https://github.com/tmcclintock/StreetViewNumbers/issues).

## Data

The SVHN data found [here](http://ufldl.stanford.edu/housenumbers/) are publically available (for non-commercial use). To replicate the work in the notebooks in this repository, you must download the data and put it in the `data` directory (no processing necessary).

## Usage

TBD