Implemented OCR and pdfminer for PDF data extraction

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Implemented OCR and pdfminer for PDF data extraction

  • Day: 2023-08-07
  • Time: 15:10 to 16:10
  • Project: Dev
  • Workspace: WP 2: Operational
  • Status: Completed
  • Priority: MEDIUM
  • Assignee: Matías Nehuen Iglesias
  • Tags: Pdf Processing, OCR, Data Extraction, Python, Text Parsing

Description

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.

Evidence

  • source_file=2023-08-07.sessions.jsonl, line_number=0, event_count=0, session_id=3364454d95d1b20d801190f432719109d01f01dda7dd6d16171e2d560bd01b7e
  • event_ids: []

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