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image-ocr

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Updated 10-15 19:53

About this skill

Image OCR

Extract text from images using Tesseract OCR via Python.

When to use this skill

  • User asks to read or extract text from an image
  • User has a screenshot with text they want to process
  • User has scanned documents that need text extraction
  • User wants to digitize text from photos

Scripts overview

Script Purpose Dependencies
ocr_extract.py Extract text from images with multiple options pytesseract, Pillow

Steps

1. Install dependencies (first time only)

Install the Python packages:

pip install pytesseract Pillow

Install Tesseract OCR engine:

For additional language support:

  • Windows: Select languages during installation
  • Linux: sudo apt install tesseract-ocr-chi-sim (Chinese Simplified), tesseract-ocr-jpn (Japanese), etc.

CRITICAL — Dependency Error Recovery: If the script fails with an ImportError or "tesseract not found" error, install the missing dependencies using the commands above, then re-run the EXACT SAME script command that failed.

2. Extract text from an image

python scripts/ocr_extract.py "IMAGE_PATH"

Options:

  • --lang LANG — OCR language (default: eng). Use chi_sim for Chinese, jpn for Japanese, eng+chi_sim for multiple.
  • --save OUTPUT_PATH — Save extracted text to a file
  • --preprocess MODE — Image preprocessing: none (default), grayscale, threshold, blur
  • --dpi DPI — Set image DPI for better accuracy (default: auto-detect)
  • --psm MODE — Tesseract page segmentation mode (0-13, default: 3 = auto)

Examples:

# Basic text extraction
python scripts/ocr_extract.py "screenshot.png"

# Chinese text extraction
python scripts/ocr_extract.py "document.jpg" --lang chi_sim

# Mixed English and Chinese
python scripts/ocr_extract.py "mixed.png" --lang eng+chi_sim

# Preprocess noisy image for better accuracy
python scripts/ocr_extract.py "noisy_scan.png" --preprocess threshold

# Save output to file
python scripts/ocr_extract.py "scan.tiff" --save output.txt

# Single line of text (e.g., license plate, serial number)
python scripts/ocr_extract.py "plate.jpg" --psm 7

Page Segmentation Modes (PSM)

Mode Description Use Case
3 Fully automatic (default) General documents
4 Assume single column Single-column text
6 Assume single block Uniform text block
7 Single line One line of text
8 Single word One word
11 Sparse text Text scattered on image
13 Raw line Single line, no OSD

Edge cases

  • Low quality images: Use --preprocess threshold or --preprocess blur to improve results
  • Rotated text: Tesseract handles slight rotation; for heavily rotated images, rotate first
  • Very small text: Increase DPI with --dpi 300 or higher
  • Mixed languages: Combine with +, e.g., --lang eng+chi_sim+jpn
  • Empty results: Try different PSM modes or preprocessing options

Scripts