{"id":20206027,"url":"https://github.com/vishalshenoy2002/digit-recognition","last_synced_at":"2026-04-10T12:31:12.159Z","repository":{"id":118164831,"uuid":"449302224","full_name":"VishalShenoy2002/Digit-Recognition","owner":"VishalShenoy2002","description":"This a Machine Learning Project which Recognises handwritten digits .i.e. 0 to 9. 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It uses the following Python Libraries:\n1. Tensorflow\n2. Numpy\n3. OS\n4. Open Cv (cv2)\n5. Matplotlib\n\n\n## Steps to Follow\n\nPlease Follow the following steps to run the program without error:\n1. First Check if all the modules or libraries are installed. If not run the following command\n``` \npip install tensorflow numpy opencv-python matplotlib\n```\n\n2. Once all the Libraries or Modules are Installed run the following command. This will train and creae the model and will save it\n```\npython .\\model.py\n```\n\n\n3. Once the Model is created, run the main program.\n```\npython .\\main.py\n```\n\n***Note:***\nThe Pics folder contains 85 hand written images which are of 28x28 pixels in size.\nIf you want to use your own pictures you can add it in the Pics folder but make sure it is 28x28 pixels in size.\n\n\n\n## Summary Of the Model\nThe Model has 3 Layers. They are as follows:\n1. Flatten Layer\n2. Dense Layer\n3. Dense Layer\n\nThe Summary Table is given below\n\n\n```\nModel: \"sequential\"\n_________________________________________________________________\n Layer (type)                Output Shape              Param #\n=================================================================\n flatten (Flatten)           (None, 784)               0\n\n dense (Dense)               (None, 128)               100480\n\n dense_1 (Dense)             (None, 10)                1290\n\n=================================================================\nTotal params: 101,770\nTrainable params: 101,770\nNon-trainable params: 0\n_________________________________________________________________\n```\n\n## Suggestion\n\nIf you are running a device with 32 bit architecture or a device with less RAM you can use the Google Colab or [Google Colabtory](https://colab.research.google.com/?utm_source=scs-index) because while training the model, it will consume over 80% of you CPU and RAM.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvishalshenoy2002%2Fdigit-recognition","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvishalshenoy2002%2Fdigit-recognition","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvishalshenoy2002%2Fdigit-recognition/lists"}