aidotnet/image-ocr

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:

• Windows: Download installer from https://github.com/UB-Mannheim/tesseract/wiki
• macOS: brew install tesseract
• Linux (Ubuntu/Debian): sudo apt install tesseract-ocr
• Linux (Fedora): sudo dnf install tesseract

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

• ocr_extract.py — Extract text from images using Tesseract OCR