https://github.com/harshoza36/realtime_digitrecognizer

A real time digit recognizer using machine learning and OpenCV
https://github.com/harshoza36/realtime_digitrecognizer

bounded-boxes deep-learning opencv realtime-digitrecognizer

Last synced: 2 months ago
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A real time digit recognizer using machine learning and OpenCV

• Host: GitHub
• URL: https://github.com/harshoza36/realtime_digitrecognizer
• Owner: HarshOza36
• License: mit
• Created: 2020-05-21T10:07:01.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2020-06-02T01:26:40.000Z (about 6 years ago)
• Last Synced: 2025-06-09T13:52:38.864Z (about 1 year ago)
• Topics: bounded-boxes, deep-learning, opencv, realtime-digitrecognizer
• Language: Jupyter Notebook
• Size: 14.6 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# RealTime_DigitRecognizer

![Project license](https://img.shields.io/apm/l/vim-mode?style=plastic)

![Python 3.6](https://img.shields.io/badge/python-3.6-blue.svg)

A real time digit recognizer using machine learning and OpenCV

We will first create single digit rercognition in ```RealTimeDigitRecognition.ipynb``` and then multidigit recognition in ```RealTimeMultiDigitRecognition.ipynb```

# Final output in ```RealTimeMultiDigitRecognition.ipynb``` will be



  



First we need to train the model in ```RealTimeDRTraining.ipynb```.

We required the dataset for training which is ```Data.zip``` extract it in the same folder where you have all the files.

And Train the model.

But if you dont want to train and directly test,the model is still provided to be used directly.

To test the model use ```RealTimeDigitRecognition.ipynb```

First we will create a normal real time detection without bounded boxes



  



Then we will add bounded boxes and ouput will be as shown below.



  



## Changes Required:

~~- Bounded Boxes accuracy needs to be increased~~

## All changes updated and created MultiDigit Recognition ipynb file