Machine Learning-Driven Document Classification and Automated File Organization Using Text Analysis

The proposed framework is a solution for classifying and orga-nizing documents.. There are some things that can be done to make it better in the future. One thing that can be done is to use transformer-based Natural Language Processing models and deep learning ap-proaches. Using models like BERT and neural network-based classifiers can help the framework understand what documents are about and classify them accurately especially for complex documents. Another thing that would be helpful is to add Optical Char-acter Recognition (OCR) functionality. This means the frame-work can look at scanned and handwritten documents and make sense of them which would be really useful for real-world doc-ument repositories. It would also be an idea to make the framework work with cloud-based storage. This way people can get to the documents from anywhere and from any device. The framework should also be able to classify documents in different languages. A lot of organizations have to deal with documents, in languages so it would be really helpful if the framework could handle this. The document classification framework may also be able to learn on its own. This means the model gets better over time because it is getting feedback from users and new examples to learn from. This would help the framework keep classifying documents over time.

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