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This is a RealTime-MultiplevDigitRecognition application that can predict output corresponding to handwritten images. Here, I used LinearSVC(support vector classifier) and the sequential model of Keras for creating this predictive model. I trained SVC and reshaped it to the 28X28 MNIST dataset, the accuracy of this model turned out to be very good when I run this model.

License: Apache License 2.0

Python 100.00%
digit-recognition digit-recognition-application machine-learning mnist-dataset

realtime-digit-recognizer's Introduction

Real-time Digit Recognizer

Handwriting digit recognition application is used in different tasks of our real-life time purposes. Precisely, it is used in vehicle number plate detection, banks for reading checks, post offices for sorting letter, and many other related tasks.

I trained the model in the local enviornment using train_main.py file and stored the predictions in the digits.pkl file and then the results are predicted using a GUI.

Example

  • Recognizing handwritten 3
    3

  • Recognizing handwritten 0,1,2
    0,1,2




Author:

Hey, This is Hrugved Kolhe.


Hrugved Kolhe

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