This thesis aims to develop a comprehensive ETL (Extract, Transform, Load) pipeline designed to process unstructured data from various document formats, such as CVs, personal letters, and application forms. The system will leverage a combination of Optical Character Recognition (OCR) to digitize scanned or handwritten documents and large language models (LLMs) to extract, interpret, and map the unstructured data into structured formats. The objective is to automate data extraction workflows, transforming raw, unstructured input into structured, machine-readable output suitable for databases and decision-support systems.

Key Components

1. Unstructured Data Extraction from Documents
   • Focus on extracting key information from unstructured documents like CVs, cover letters, and application forms.
   • Use LLMs for entity recognition (e.g., names, addresses, education, job titles) and classification of document sections based on content (e.g., work experience, skills).
   • Address variability in document structure, ambiguous information, and contextual interpretation.
2. Optical Character Recognition (OCR)
   • Implement OCR to digitize text from scanned documents or handwritten forms.
   • Utilize preprocessing techniques to clean OCR outputs, handling noise from varying fonts, layouts, and image quality.
   • Use LLMs to correct OCR errors and enhance accuracy during the transformation phase.
3. Data Transformation and Mapping to Structured Formats
   • Employ LLMs to contextualize and structure the extracted data.
   • Define data mapping rules to convert unstructured inputs into standardized fields, such as names, dates, skills, and qualifications.
   • Automate the transformation process using machine learning models to handle diverse document formats and content types.
4. ETL Pipeline Automation
   • Build a fully automated pipeline capable of processing large volumes of documents in real-time.
   • Manage extraction (via OCR), transformation (with LLMs), and loading (structured data storage) seamlessly within an enterprise setting.
   • Develop feedback loops for continuous improvement of data extraction models and correction mechanisms.

Challenges

• Ensuring high accuracy in OCR output, especially from handwritten or poorly formatted documents.
• Training LLMs to handle diverse document types and ambiguous language in unstructured text.
• Mapping unstructured data fields to structured formats that vary by domain (e.g., HR, finance, healthcare).

Applications

• Automating HR processes (e.g., recruitment, application filtering) by extracting relevant information from CVs and cover letters.
• Streamlining data entry in sectors such as banking, insurance, or healthcare.
• Digitizing and structuring archival documents and large-scale records for research or enterprise use.