📅 2023-08-07 — Session: Implemented OCR and pdfminer for PDF data extraction
🕒 15:10–16:10
🏷️ Labels: Pdf Processing, OCR, Data Extraction, Python, Text Parsing
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to improve the extraction of structured data from PDF documents, focusing on overcoming challenges with text encoding and font management.
Key Activities
- Explored the structure of PDF documents to identify methods for data extraction.
- Discussed the use of Optical Character Recognition (OCR) to handle non-standard symbols and images within PDFs.
- Analyzed PDF encoding and font issues, including errors in accessing font information using PyPDF2.
- Transitioned from PyPDF2 to the pdfminer library for robust text extraction, providing code snippets for implementation.
- Developed Python code for parsing text into structured data, handling multiple pages, and filtering empty lines.
- Addressed specific text processing challenges, such as handling form feed characters and parsing non-standard sections.
Achievements
- Successfully extracted table data from PDFs using OCR and pdfminer.
- Resolved indirect font object errors and improved text extraction accuracy.
- Implemented a comprehensive solution for parsing structured text into DataFrames using Python.
Pending Tasks
- Further refinement of OCR techniques to enhance accuracy and efficiency.
- Exploration of additional libraries or tools to streamline PDF data extraction processes.