Organizations deal with thousands of unstructured documents daily—resumes, emails, research papers, and reports. Manually classifying and analyzing these files is time-consuming and error-prone. To address this challenge, the AI-Based Document Analyzer (Document Intelligence System) leverages Optical Character Recognition (OCR), Deep Learning, and Natural Language Processing (NLP) to automatically extract insights from documents.
This project is ideal for students, researchers, and enterprises who want to explore real-world applications of AI in automating document workflows.
Project Overview
The AI Document Analyzer is a web-based system that accepts documents in PDF or image formats and performs a three-stage analysis:
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Document Classification – Identifies the type of document (Resume, Email, Research Paper, etc.) using a TensorFlow Lite model.
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Text Extraction – Extracts textual content with PaddleOCR for high accuracy.
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Intelligent Analysis – Applies a language model to generate context-aware summaries or actions (e.g., evaluating a resume, drafting an email reply).
The results are displayed on an easy-to-use Streamlit web interface, making it accessible for non-technical users.
Features & Functionality
✅ Multi-Format Document Support – Accepts PDF, JPG, JPEG, PNG.
✅ Automatic Document Classification – Distinguishes between resumes, emails, and research papers.
✅ High-Accuracy OCR – Extracts structured text from images with PaddleOCR.
✅ Context-Aware Summarization – Generates insights tailored to document type (resume analysis, email draft, etc.).
✅ Multi-Page PDF Support – Processes all pages of lengthy PDFs sequentially.
✅ User-Friendly Web Interface – Simple drag-and-drop upload using Streamlit.
Tech Stack
- Programming Language: Python
- Machine Learning Libraries: TensorFlow Lite (classification), PyTorch, Transformers (NLP)
- OCR Engine: PaddleOCR
- Web Framework: Streamlit
- PDF/Image Processing: Poppler, pdf2image, OpenCV
- Deployment: Local/Cloud with GPU support
System Workflow
- Upload Document → User uploads PDF/image in the web app.
- Classification Engine → TensorFlow Lite model predicts document type.
- OCR Engine → PaddleOCR extracts all text.
- NLP Analysis → Hugging Face model generates summary/insights.
- Display Results → Output (classification + extracted text + insights) shown in UI.
Conclusion
The AI-Powered Document Analyzer demonstrates how OCR, Machine Learning, and NLP can transform unstructured documents into structured insights. From resumes to research papers, this project reduces human effort, saves time, and provides real-time analysis with an intuitive interface.
This project is a great fit for final-year students, AI/ML researchers, and enterprises aiming to integrate automation into their workflows.
