DeepLearning_Alphabet

Jaram DeepLearning Study - Recognition of Handwritten Alphabet.

Getting Started

1. Clone this repository

cd workspace
git clone https://github.com/Jaram2019/DeepLearning_Alphabet

2. Activate python virtual environment

cd DeepLearning_Alphabet
sudo pip install virtualenv
virtualenv -p python3 .env  # Create a virtual environment (python3)
source .env/bin/activate  # Activate the virtual environment

3. Install requirements

pip install -r requirements.txt

4. Deactivate the virtual environment

deactivate  # Exit the virtual environment

Requirements

virtualenv>=16.3.0
python>=3.6
pandas==0.23.4
numpy==1.15.1
pandas==0.23.4
numpy==1.15.1
matplotlib==2.2.3
tensorflow>=1.13.0rc (build for CPU-only)
tensorflow-gpu (build with GPU support)

Project

A-Z Handwritten Alphabets in .csv format

Content

The dataset contains 26 folders (A-Z) containing handwritten images in size 2828 pixels, each alphabet in the image is centre fitted to 2020 pixel box.

Each image is stored as Gray-level

Acknowledgements

The images are taken from NIST(https://www.nist.gov/srd/nist-special-database-19) and NMIST large dataset and few other sources which were then formatted as mentioned above.