https://github.com/imdeep2905/digit-recognizer

This is a little project which implements ML model (trained on "Hello World of ML" - MNIST Digit Dataset) in real life.
https://github.com/imdeep2905/digit-recognizer

convolutional-neural-network deep-learning keras mnist-classification mnist-handwriting-recognition opencv-python python tensorflow tkinter

Last synced: 25 days ago
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This is a little project which implements ML model (trained on "Hello World of ML" - MNIST Digit Dataset) in real life.

• Host: GitHub
• URL: https://github.com/imdeep2905/digit-recognizer
• Owner: imdeep2905
• License: mit
• Created: 2020-03-23T07:01:16.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2021-04-29T22:23:29.000Z (almost 4 years ago)
• Last Synced: 2024-11-19T12:29:17.598Z (3 months ago)
• Topics: convolutional-neural-network, deep-learning, keras, mnist-classification, mnist-handwriting-recognition, opencv-python, python, tensorflow, tkinter
• Language: Python
• Homepage:
• Size: 22.9 MB
• Stars: 3
• Watchers: 2
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Digit-Recognizer

![Demo](https://github.com/imdeep2905/Digit-Recognizer/blob/master/Images/demo.gif)


# Contents

1. [Introduction](#Introduction)

2. [How to run](#How-to-run) 

3. [For ML nerds](#For-all-ML-nerds)

4. [Credits](#Credits)

# Introduction

This is a little project which implements ML model (trained on **"Hello World of ML" - MNIST Digit Dataset**) in real life. You can hand draw digit in GUI, Browse an exsisting image and open your webcam and capture digit. Although model is really unstable with noisy images.(Maybe i'll try to improve it in future.)


Some screenshots of Digit Recognizer:


![Main Screen](https://github.com/imdeep2905/Digit-Recognizer/blob/master/Images/mainscreen.PNG)


![Main Screen2](https://github.com/imdeep2905/Digit-Recognizer/blob/master/Images/mainscreen2.PNG)


![Main Screen3](https://github.com/imdeep2905/Digit-Recognizer/blob/master/Images/mainscreen3.PNG)


# How to run

  1. Fulfill ```requirements.txt``` (i.e. ```pip install -r requirements.txt```).

  2. Run with command ```python main.py```.

# For all ML nerds

For all the ML nerds out there model was trained on following architecture:

```

model = keras.models.Sequential([

keras.layers.Conv2D(64, (2, 2), padding = 'same' , activation = 'elu', input_shape = [28, 28, 1]),

keras.layers.MaxPooling2D(2),

keras.layers.Conv2D(128, (3, 3), padding = "same", activation = "elu"),

keras.layers.Conv2D(256, (4, 4), padding = "same", activation = "elu"),

keras.layers.Dropout(0.5),

keras.layers.MaxPooling2D(2),

keras.layers.Flatten(),

keras.layers.Dense(128, activation = "elu"),

keras.layers.Dropout(0.25),

keras.layers.Dense(10, activation = "softmax")

])

model.compile(loss = "sparse_categorical_crossentropy", metrics = ["accuracy"], optimizer = keras.optimizers.SGD(lr = 0.08))

``` 

It's accuracy was around ```0.99``` on training data and ```0.95-0.97(not sure)``` on testing data.


At last you can load pretrained model (```model.h5```) and play with it.

# Credits

Contributors :computer: : 

   * [Deep Raval](https://github.com/imdeep2905)

Without these excellant libraries :heart: this would not have been possible.

   * tensorflow

   * pillow

   * opencv-python

   * numpy