https://github.com/sajalkmr/notesseparator

A simple deep learning model to separate Handwritten notes and non notes images with Python and Tensorflow
https://github.com/sajalkmr/notesseparator

deep-learning image-classification machine-learning python tensorflow

Last synced: about 2 months ago
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A simple deep learning model to separate Handwritten notes and non notes images with Python and Tensorflow

• Host: GitHub
• URL: https://github.com/sajalkmr/notesseparator
• Owner: sajalkmr
• License: mit
• Created: 2023-01-04T18:07:46.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2023-03-18T19:05:27.000Z (over 3 years ago)
• Last Synced: 2025-01-22T08:12:43.669Z (over 1 year ago)
• Topics: deep-learning, image-classification, machine-learning, python, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 39.8 MB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

          
# HandwrittenNotesClassification

A deep learning model to separate Handwritten notes and non notes images with Python and Tensorflow.

Requirements



	
Python 3.6+

	
TensorFlow 2.0+

	
OpenCV 4.0+

  
Numpy

  
Json



Usage



	
Clone this repository to your local machine.

	
Install the required packages listed in the requirements.txt file by running pip install -r requirements.txt.

	
Place the images you want to classify in the test_images directory.

	
Put your model in the models directory. The model should be saved in the .h5 format.

	
Run the NotesSeparator.py script to classify the images: NotesSeparator.ipynb if you are running in Jupyter Notebook.

	
The classified images will be saved in the output directory.



Example File Structure


	├── classify_notes.py

	├── models

	│   └── notesmodel.h5

	├── output

	│   ├── non_notes

	│   ├── notes

	│   └── results

	│       ├── image1.png

	│       ├── image1.json

	│       ├── image2.png

	│       ├── image2.json

	│       └── results.json

	├── README.md

	└── test_images

	    ├── image1.jpg

	    └── image2.jpg



Model Architecture

The model used for this project is a Convolutional Neural Network (CNN) with the following layers:



	
Conv2D layer with 16 filters and a kernel size of (3,3)

	
MaxPooling2D layer

	
Conv2D layer with 32 filters and a kernel size of (3,3)

	
MaxPooling2D layer

	
Conv2D layer with 16 filters and a kernel size of (3,3)

	
MaxPooling2D layer

	
Flatten layer

	
Dense layer with 256 neurons

	
Dense layer with 1 neuron (output layer) with a sigmoid activation function



Files Saved After Classification

The classified images are saved in either the notes or non_notes directory in the output folder depending on their classification. Additionally, a results.json file is created in the output/results folder that contains the results of the classification for each image.


Contributing

Contributions are welcome. Please fork the repository and submit a pull request.


License

This project is licensed under the MIT License.