image-ocr

name	image-ocr
description	Extract text from images using Python OCR. Use when the user wants to read text from screenshots, photos of documents, scanned pages, or any image containing text. Supports PNG, JPEG, TIFF, BMP, and WebP formats.
compatibility	Requires Python 3 and pytesseract + Pillow (pip install pytesseract Pillow). Also requires Tesseract OCR engine installed on the system.

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