Batch Extract Unified Social Credit Codes from PDFs Using Python

Preface

When helping a follower with a simple requirement to batch‑extract target information from PDF files, I realized the solution could benefit many others, so I share it here.

Requirement Clarification

The task involves dozens of PDF files, each containing a unified social credit code. The goal is to extract the code (a mix of numbers and letters) from every file.

Implementation Process

First, use pdfminer to read a PDF and a regular expression to locate the credit code. The single‑file extraction code is:

from pdfminer import high_level
import re

text = high_level.extract_text('1.pdf')  # extract text from PDF
regex = r'统一社会信用代码：(.*?)
'
credit_codes = re.findall(regex, text)
print(credit_codes)

After confirming the single‑file approach works, the batch processing script iterates over all PDF files in a directory, extracts the text, applies the same regex, and prints each code:

from pdfminer import high_level
from pdfminer.layout import LTTextContainer
import re
import os

for root, dirs, files in os.walk('./'):
    for f in files:
        file_name = os.path.join(root, f)
        if file_name.endswith('.pdf'):
            text = high_level.extract_text(file_name)
            regex = r'统一社会信用代码：(.*?)
'
            credit_codes = re.findall(regex, text)
            print(credit_codes[0])

Running the script prints the extracted codes for all PDFs, as shown in the following screenshot:

Conclusion

This guide provides a practical example of batch‑extracting specific information from PDF documents using Python, pdfminer, and regular expressions, enabling readers to adapt the approach for similar data‑extraction tasks.