Intelligent PDF Data Extraction and database creation

Project Goal: Develop a system for extracting structured and unstructured data from vendor-supplied PDFs, storing it in a database for efficient search and retrieval, and integrating a chatbot for natural language querying of the extracted information.

Project Scope:

• Input: Diversely structured PDFs (text, headings, paragraphs, tables, bullet points) including RFQs, contracts, manuals, and reports.

• Key Functions:

  • Accurate data extraction, excluding irrelevant headers/footers.
  • Precise table recognition and structuring, linking tables to their bold-text titles (typically followed by a colon). Handles nested table data.
  • Extraction and organization of bullet points as nested lists.
  • Dynamic text structuring using headings as keys and corresponding text as values.
  • Data cleaning (symbol removal, space normalization).

• Data Management & Querying:

  • Elasticsearch for indexing and searching.
  • Database schema accommodating structured (tables) and unstructured (text) data.

Technical Challenges & Solutions:

• Data Accuracy: Employ advanced NLP techniques (e.g., spaCy, Stanford CoreNLP) for improved accuracy in identifying headings, tables, and bullet points. Consider using machine learning models trained on sample PDFs to enhance accuracy.

• Header/Footer Removal: Implement more sophisticated header/footer detection using techniques like comparing line spacing and font sizes across multiple pages to identify consistent patterns. Explore using pre-trained models for document layout analysis.

• **Table

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