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        "author": "Silas Bischoff",
        "author_email": "silas.bischoff@googlemail.com",
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        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "Intended Audience :: Financial and Insurance Industry",
            "Intended Audience :: Legal Industry",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Scientific/Engineering :: Image Recognition",
            "Topic :: Software Development :: Libraries",
            "Topic :: Software Development :: Libraries :: Python Modules"
        ],
        "description": "# basic-document-classifier\nA simple CNN for n-class classification of document images.\n\nIt doesn't take colour into account (it transforms to grayscale).\nFor small numbers of classes (2 to 4) this model can achieve > 90% accuracy with as little as 10 to 30 training images per class.\nTraining data can be provided in [any image format supported by *PIL*](https://pillow.readthedocs.io/en/5.1.x/handbook/image-file-formats.html).\n\n## Installation\n\n```pip install document-classifier```\nor\n```poetry add document-classifier```\n\n## Usage\n\n```python\nfrom document_classifier import CNN\n\n# Create a classification model for 3 document classes.\nclassifier = CNN(class_number=3)\n\n# Train the model based on images stored on the file system.\ntraining_metrics = classifier.train(\n    batch_size=8,\n    epochs=40,\n    train_data_path=\"./train_data\",\n    test_data_path=\"./test_data\"\n)\n# \"./train_data\" and \"./test_data\" have to contain a subfolder for\n# each document class, e.g. \"./train_data/letter\" or \"./train_data/report\".\n\n# View training metrics like the validation accuracy on the test data.\nprint(training_metrics.history[\"val_acc\"])\n\n# Save the trained model to the file system.\nclassifier.save(model_path=\"./my_model\")\n\n# Load the model from the file system.\nclassifier = CNN.load(model_path=\"./my_model\")\n\n# Predict the class of some document image stored in the file system.\nprediction = classifier.predict(image=\"./my_image.jpg\")\n# The image parameter also taks binary image data as a bytes object.\n```\n\nThe prediction result is a 2-tuple containing the document class label as a string and the confidence score as a float.\n\n## Changes\n\n### 0.1.2\n- Give every CNN instance its own isolated tensorflow graph and session\n\n### 0.1.1\n- Fix a bug that occured when using multiple model instances at the same time\n\n## TODO\n\nThe model architecture is fixed for now and geared towards smaller numbers of classes and training images.\nI'm working on automatic scaling for the CNN.\n",
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        "keywords": "document,image,classification,deep,learning",
        "license": "MIT",
        "maintainer": "Silas Bischoff",
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        "name": "document-classifier",
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