https://github.com/brianruizy/handwritten-digit-recognition

Machine Learning - Personal Python project that will predict the value of a handwritten digit, based of a large dataset of 8x8 images.
https://github.com/brianruizy/handwritten-digit-recognition

Last synced: 4 days ago
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Machine Learning - Personal Python project that will predict the value of a handwritten digit, based of a large dataset of 8x8 images.

• Host: GitHub
• URL: https://github.com/brianruizy/handwritten-digit-recognition
• Owner: BrianRuizy
• Created: 2019-03-06T23:46:03.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2020-07-26T03:53:56.000Z (about 5 years ago)
• Last Synced: 2025-08-25T01:52:31.497Z (about 2 months ago)
• Language: Python
• Homepage:
• Size: 93.8 KB
• Stars: 2
• Watchers: 0
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

          


     

     Handwritten Digit Recognition





Personal portfolio python project that will be implementing a machine learning classification model to predict the value of a handwritten digit through supervised learning.







## About

Reading letters & numbers off of a sheet of paper is profoundly intuitive for us humans, as our visual cortex has developed and evolved for thousands of years. For computers, on the other hand, interpreting strokes from a sheet of paper algorithmically is incredibly difficult. However, due to their immense processing speed, computers can take in massive amounts of data to therefore effectively *learn* and *predict* what a particular digit means. 

The model that I've opted for this particular project is the SVM model, or *linear Support Vector classification*. Although there are different approaches it is difficult to appoint a specific model as superior, as it depends on the criteria or project scope. However, SVM is the preferred method as it allows for the fine-tuning of parameters to achieve better accuracy.

## Dependencies

The algorithm itself will be given a large dataset, [MNIST](https://www.kaggle.com/c/digit-recognizer/data), from *sci-kit learn*.

Tools include:

[SciKit-learn](https://scikit-learn.org/stable/),

[matplotlib](https://matplotlib.org/)

## Versions

**Low-Level**

In the lower-level approach, you will see how a computer can read a large data set and output the users chosen element from the data structure, in a non-robust form.

![SVM3](https://user-images.githubusercontent.com/23439187/54062221-64b08500-41ca-11e9-8023-d9b4970ec05e.png)

**High-Level**

(work in progress) In the latter, higher-level, the user will be able to simply write a digit, through an input dialog, for the computer to interpret, using additional web components. 

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#### © [Brian Ruiz](https://github.com/BrianRuizy)