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pdf-ocr

Extract text from scanned PDFs using optical character recognition. Use when a user asks to OCR a PDF, read a scanned document, extract text from an image PDF, digitize a scanned file, convert a scanned PDF to text, or read text from a photograph of a document. Supports multiple languages and handles low-quality scans.

#ocr#pdf#scanned#text-extraction#tesseract
terminal-skillsv1.0.0
Works with:claude-codeopenai-codexgemini-clicursor
Source

Usage

$
✓ Installed pdf-ocr v1.0.0

Getting Started

  1. Install the skill using the command above
  2. Open your AI coding agent (Claude Code, Codex, Gemini CLI, or Cursor)
  3. Reference the skill in your prompt
  4. The AI will use the skill's capabilities automatically

Example Prompts

  • "Summarize the key findings in quarterly-report.pdf"
  • "Extract all tables and figures from this research paper"

Documentation

Overview

Extract readable text from scanned or image-based PDF documents using optical character recognition (OCR). This skill converts PDF pages to images, runs OCR to detect text, and outputs clean structured text. Handles multi-page documents, multiple languages, and low-quality scans with preprocessing.

Instructions

When a user asks to OCR a scanned PDF or extract text from an image-based PDF, follow these steps:

Step 1: Check if OCR is actually needed

First, attempt normal text extraction. If the PDF already contains selectable text, OCR is unnecessary:

python
import pdfplumber

def check_text_content(pdf_path):
    with pdfplumber.open(pdf_path) as pdf:
        for page in pdf.pages[:3]:
            text = page.extract_text()
            if text and len(text.strip()) > 50:
                return True  # Has extractable text, OCR not needed
    return False  # Image-only PDF, needs OCR

Step 2: Install and verify dependencies

Ensure the required tools are available:

bash
# Install Tesseract OCR engine
# Ubuntu/Debian:
sudo apt-get install tesseract-ocr
# macOS:
brew install tesseract

# Install Python packages
pip install pytesseract pdf2image Pillow

# For additional languages:
sudo apt-get install tesseract-ocr-deu  # German
sudo apt-get install tesseract-ocr-fra  # French
sudo apt-get install tesseract-ocr-jpn  # Japanese

Step 3: Convert PDF pages to images

python
from pdf2image import convert_from_path

def pdf_to_images(pdf_path, dpi=300):
    images = convert_from_path(pdf_path, dpi=dpi)
    return images

Use 300 DPI for standard documents. Increase to 400-600 DPI for small text or low-quality scans.

Step 4: Preprocess images for better accuracy

Apply preprocessing to improve OCR quality:

python
from PIL import Image, ImageFilter, ImageEnhance

def preprocess_image(image):
    # Convert to grayscale
    gray = image.convert('L')
    # Increase contrast
    enhancer = ImageEnhance.Contrast(gray)
    enhanced = enhancer.enhance(2.0)
    # Sharpen
    sharpened = enhanced.filter(ImageFilter.SHARPEN)
    # Binarize (threshold)
    threshold = 150
    binary = sharpened.point(lambda x: 255 if x > threshold else 0)
    return binary

Step 5: Run OCR on each page

python
import pytesseract

def ocr_pages(images, lang='eng'):
    results = []
    for i, image in enumerate(images):
        processed = preprocess_image(image)
        text = pytesseract.image_to_string(processed, lang=lang)
        results.append({
            "page": i + 1,
            "text": text.strip(),
            "confidence": get_confidence(processed, lang)
        })
    return results

def get_confidence(image, lang='eng'):
    data = pytesseract.image_to_data(image, lang=lang, output_type=pytesseract.Output.DICT)
    confidences = [int(c) for c in data['conf'] if int(c) > 0]
    return sum(confidences) / len(confidences) if confidences else 0

Step 6: Output the results

Combine and format the extracted text. Save as a text file or return directly:

python
def save_results(results, output_path):
    with open(output_path, 'w', encoding='utf-8') as f:
        for page in results:
            f.write(f"--- Page {page['page']} (confidence: {page['confidence']:.0f}%) ---\n")
            f.write(page['text'] + "\n\n")
    return output_path

Examples

Example 1: OCR a scanned contract

User request: "Extract text from this scanned contract scan_contract.pdf"

Actions taken:

  1. Check for existing text layer - none found, OCR needed
  2. Convert 5 pages to images at 300 DPI
  3. Preprocess and run OCR in English

Output:

OCR completed for scan_contract.pdf (5 pages)

Page-by-page confidence:
  Page 1: 96% confidence
  Page 2: 94% confidence
  Page 3: 91% confidence
  Page 4: 95% confidence
  Page 5: 88% confidence (lower quality scan detected)

Output saved to: scan_contract_text.txt (4,230 words extracted)

Note: Page 5 had lower image quality. Review that page for accuracy.

Example 2: OCR a multi-language document

User request: "Read this scanned document, it's in German: rechnung.pdf"

Actions taken:

  1. Verify tesseract-ocr-deu language pack is installed
  2. Convert pages to images at 300 DPI
  3. Run OCR with lang='deu'

Output:

OCR completed for rechnung.pdf (2 pages) using German language model

  Page 1: 93% confidence
  Page 2: 95% confidence

Extracted 812 words. Output saved to: rechnung_text.txt

Example 3: Batch OCR multiple scanned files

User request: "OCR all the scanned PDFs in the ./receipts/ folder"

Actions taken:

  1. Find all PDF files in ./receipts/ (found 12 files)
  2. Check each for existing text layer
  3. Run OCR on the 10 files that need it

Output:

Batch OCR complete: 12 files processed

  Already had text: 2 files (skipped)
  OCR completed:    10 files
  Average confidence: 92%

Output files saved to ./receipts/ocr_output/
  receipt_001_text.txt (97% confidence)
  receipt_002_text.txt (94% confidence)
  ...
  receipt_010_text.txt (85% confidence - review recommended)

Guidelines

  • Always check for existing text content before running OCR. Many PDFs already have a text layer.
  • Use 300 DPI as the default resolution. Increase for small fonts or poor quality scans.
  • Report confidence scores per page so users know which pages may need manual review.
  • For multi-language documents, specify the correct Tesseract language code. Multiple languages can be combined: lang='eng+deu'.
  • Preprocess images before OCR: grayscale conversion, contrast enhancement, and binarization significantly improve accuracy.
  • For rotated or skewed scans, apply deskewing before OCR using image rotation detection.
  • Large PDFs should be processed page by page to manage memory usage.
  • Common Tesseract language codes: eng (English), deu (German), fra (French), spa (Spanish), jpn (Japanese), chi_sim (Chinese Simplified), kor (Korean).

Information

Version
1.0.0
Author
terminal-skills
Category
Documents
License
Apache-2.0

Use Cases

→Extract Intelligence from PDF Archives