📅 2023-08-07 — Session: Implemented OCR and pdfminer for PDF data extraction
🕒 15:10–16:10
🏷️ Labels: Pdf Processing, OCR, Pdfminer, Data Extraction, Python
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The goal of this session was to address challenges in extracting data from PDF documents, specifically focusing on handling non-standard symbols and encoding issues, and transitioning to more effective libraries for text extraction.
Key Activities
- PDF Structure Analysis: Investigated the structure and encoding of PDF documents to understand the limitations of direct text extraction.
- OCR Implementation: Explored the use of Optical Character Recognition (OCR) to extract text from images within PDFs, especially for tables.
- Library Transition: Moved from using PyPDF2 to pdfminer for a more robust text extraction process, including handling indirect font objects and encoding issues.
- Code Development: Developed Python code snippets for extracting structured data from PDFs, including parsing titles, names, and degrees into a pandas DataFrame.
Achievements
- Successfully transitioned to using pdfminer, which resolved previous limitations faced with PyPDF2.
- Implemented OCR to handle non-standard text symbols and extract data from images within PDFs.
- Developed and refined Python scripts to parse and structure extracted data into DataFrames.
Pending Tasks
- Further optimization of OCR processes to improve accuracy and efficiency.
- Exploration of additional libraries or tools that may enhance PDF data extraction capabilities.