This project is an AI agent trained using KNN, Neural Network (NN), and Convolutional Neural Network (CNN) models to recognize handwriting numbers. The numbers are drawn in real-time by holding down the left mouse button in the Pygame window. Please read my project descriptions here https://theo-xiao-sg.github.io/handwriting_recognition.html

This project is based on the Python programming language and primarily utilizes standard libraries like TensorFlow, Pillow, scikit-learn, joblib, pgzero, and pygame. Please note that the Keras library is automatically installed together with TensorFlow as well. Keras is a high-level neural networks API.

Download the requirements.txt and install the required Python libraries. Please note all my 4 projects share the same requirements.txt. If you have done the installation for one project, you can skip it for the other 3 projects

# install all packages using requirements.txt
python -m pip install -r requirements.txt

• If you want to train your model, you can run KNN_model.py, neural_network_model.py, or CNN_model.py in the folder. The model will train itself using 70% of the total 1447 images in the folder images_training. I have selected the best model parameters for KNN and NN models in the codes but you can always try yourselves with a set of parameters. For KNN_model.py, you can amend the line 29. For neural_network_model.py, you can amend the line 31. For CNN_model.py, I haven't done much of hyperparameter tuning. But I am quite happy with the hyperparameter set since the accuracy is around 98%-99%.

• Then, the trained model file saved in a pickle file, either handwriting_knn.pkl, handwriting_knn.pkl, or cnn.pkl, will be generated in the folder.

• To try CNN model, you need to run testing_cnn.py, and then a pygame window will show up. The CNN model is the best in the three models and acchieve 98-99% accuracy in this case. You should definitely try it.

• To try KNN and NN models, you need to run testing.py since the images are reshaped differently from CNN. In the line 8 of testing.py, I have chosen the model which I prefer and it is a Neural Network model. If you like, you can try the other KNN model or any model you trained by amending the line 7 or 8 of testing.py.

• Hold down the left mouse button and draw a number from 0 to 9, and click the key Detect, then you get the recognized number using the AI model you just trained. I hope it is a correct recognition. For me, most of the tests came with correct answers.

• Then, please click the key Clear to clear the canvas before you try the next number.

• Try all the numbers you like.

• I hope you are impressed by this tool by now!

• I have made an animated image file handwriting_recog_demo.gif and a video file handwriting_recog_demo.mp4 to illustrate the number recognition tool. Both files are saved in the folder results.

• You can go to the folder results and simply click the file handwriting_recog_demo.gif in Github. You can immediately see how powerful this number recognition tool is.

• If you download everything and use windows pc, you can open handwriting_recog_demo.mp4 by Media Player, or most browsers, including Chrome and Edge.

• If you download everything and use a Windows pc, you can open handwriting_recog_demo.gif by Photos, or most browsers, including Chrome and Edge. You should not use Paint since it only opens the 1st image.

• I hope you are impressed by this tool as I was!