How can I extract text from scanned PDFs for CV parsing in my HR tool?

Below is a recommended approach to handle scanned PDFs in your HR/CV parsing tool:

1. Use an OCR Layer

Since scanned PDFs are essentially images of text, you’ll need to perform OCR (Optical Character Recognition) to extract the text. ChatGPT’s text API or even GPT-4’s current capabilities don’t directly process image data embedded within PDFs. ChatGPT Vision may offer image understanding, but it isn’t generally available as an API for document processing and may not be optimized for lengthy documents like CVs.

2. OCR Providers and Tools

You have a few options:

• Open-Source OCR:
  Tesseract OCR is open source and widely used. It integrates well with many programming languages (e.g., via pytesseract in Python).

• Commercial Services:
  If you need enhanced accuracy or additional features (like layout preservation), you might consider commercial OCR services such as:

  • IBM Watson
  • ABBYY FlexiCapture
  • Google Cloud Vision
  • AWS Textract

3. Workflow Integration

A typical workflow could be:

1. PDF Preprocessing:
   Extract the pages or images from the PDF. Libraries like pdf2image in Python can convert PDF pages to images.

2. Run OCR:
   Process the images with an OCR tool (e.g., Tesseract or a commercial API) to extract text.

3. Post-Processing:
   Clean and structure the OCR results as needed. You might want to parse key information (like names, skills, experiences) from the text.

4. ChatGPT API:
   Once you have the text, use the ChatGPT API to further process or analyze the CV content as needed for your tool.

4. Example Using Python

Here’s a brief example in Python that uses pdf2image and pytesseract:

PYTHONCopied
from pdf2image import convert_from_path
import pytesseract

# Convert PDF pages to images
pages = convert_from_path('path/to/scanned_cv.pdf', dpi=300)

# Initialize a variable to hold the complete text
extracted_text = ""

# Process each page using Tesseract OCR
for page in pages:
    text = pytesseract.image_to_string(page)
    extracted_text += text + "\n"

# Now you can feed extracted_text to ChatGPT for further processing
print(extracted_text)

Final Thoughts

Since ChatGPT Vision doesn’t offer a direct API for processing scanned documents, incorporating an external OCR tool into your pipeline is the best solution. This modular approach lets you use specialized OCR solutions for text extraction while leveraging ChatGPT for natural language processing and analysis afterward.

If you have further questions or need more detailed guidance on any of these steps, feel free to ask!