Enhanced PDF Text and Transaction Parsing

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Enhanced PDF Text and Transaction Parsing

  • Day: 2024-12-22
  • Time: 23:15 to 23:55
  • Project: Dev
  • Workspace: WP 2: Operational
  • Status: Completed
  • Priority: MEDIUM
  • Assignee: Matías Nehuen Iglesias
  • Tags: Pdf Parsing, Regex, OCR, Data Extraction, Python

Description

Session Goal

The session aimed to improve the parsing of financial statements, focusing on extracting text from PDFs and refining transaction parsing logic using regex.

Key Activities

  • Developed strategies for parsing financial statements using regex, targeting regular and installment transactions.
  • Debugged PDF parsing issues, addressing regex misalignment, and explored OCR for text extraction.
  • Implemented text extraction techniques using PyPDF2, pdfminer, and pdfplumber, focusing on maintaining layout and line breaks.
  • Updated regex patterns for transaction parsing, ensuring accurate data extraction and handling of installment transactions.
  • Resolved directory path errors in scripts and converted Spanish month abbreviations to datetime in Pandas.

Achievements

  • Successfully extracted text from PDFs using OCR and refined regex patterns for transaction parsing.
  • Improved PDF text extraction scripts to handle layout and line breaks effectively.
  • Enhanced transaction parsing logic to accurately capture transaction details, including dates and installments.

Pending Tasks

  • Further refine regex patterns to handle edge cases in transaction parsing.
  • Explore additional PDF extraction libraries or tools for improved accuracy.

Evidence

  • source_file=2024-12-22.sessions.jsonl, line_number=2, event_count=0, session_id=831f6246c649f0ca2a30b97627ca1a5b2e3fa7266a7d102f2c4340d0b1e1c750
  • event_ids: []

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