Explore the EMNIST letter dataset with various dimensionality reduction techniques and visualizations, and then create an ML model to predict handwritten letters.

Project Writeup

Project Presentation Slides

Project Presentation Video

Heroku Demo Link - may be in active in the future

Colab Demo - same as Heroku App but can be run straight from Colab

1. EMNIST Letter Dataset
2. Dimensionality Reduction Techniques
3. ML Model
4. User Application
5. Web Deployment

When exploring various was to import the EMNIST dataset we came across a pre-made Python library called emnist which made loading the various sets of data into our project straight forward and simple. After a few custom functions we were able to generate quick visualizations of the letters along with their labels.

During the data exploration stage we applied several dimensionality reduction techniques to better visualize how the letter images could be clustered together. The highlights are PCA (Principal Component Analysis) which is a good baseline technique and UMAP (Uniform Manifold Approximation and Projection) which provide clearer clusters and better separation. Images shown below and full code can be found in the dev_notebooks folder.

After creating a few different versions of a Tensorflow Deep Neural Net we ultimately used an architecture that did well in a Kaggle competition with the 0-9 MNIST digit data set.

To create an interactive experience for users to draw handwritten digits and provide model prediction in real time, we used Streamlit to create our front end application. This demo can easily be run in out Streamlit_App.ipynb Colab notebook.

To make our Letter drawing prediction application as accessible as possible we created a self contained web app and deployed it using Heroku which can be found in the heroku_app folder.

Initial Demo Link

coryroyce akanksha0911 AbrahamKong Karishma-Kuria

Live Demo Video: https://youtu.be/wI2hDtuRbUw

Project PPT Link: https://docs.google.com/presentation/d/1svfLKvYqRFLc2mmICwIRXpxxdTbY63B64NXi0cnEuyM/edit#slide=id.g106ebbdb28d_0_51

Project Report Link: https://docs.google.com/document/d/1S3f0IWZUfYTdzhxm7Hy8ESF2y3pgSkd_AvRUsdQ6OI0

Original EMNIST Paper

EMNIST dataset: https://www.nist.gov/itl/iad/image-group/emnist-dataset

Direct download: http://www.itl.nist.gov/iaui/vip/cs_links/EMNIST/gzip.zip

Cohen, G., Afshar, S., Tapson, J., & van Schaik, A. (2017). EMNIST: an extension of MNIST to handwritten letters. Retrieved from http://arxiv.org/abs/1702.05373

Importing and formatting Image data inspired by ArangurenAndres/EMNSIT-Image-classification

Mapping and original file reference Website

Parts of the visualization nad PCA were inspired by Rahul228646's Kaggle notebook

Example Data Visualizations for Images from Kaggle Notebook

Image Classification in 10 Minutes with MNIST Dataset Article

How to Develop a CNN for MNIST Article

Kaggle Competition with MNIST 0-9 digits Article