When dealing with text within an image, the system automatically recognizes it as an OCR (Optical Character Recognition) task and applies the corresponding capabilities.

OCR Tool Skill

Use Tesseract OCR to extract text from images, particularly useful for financial charts, announcements, reports, and screenshots containing Chinese and English text.

When to Use

✅ USE this skill when:

When NOT to Use

❌ DON'T use this skill when:

Setup

# Verify Tesseract installation
tesseract --version
Install language packs if needed (for Chinese)
Windows: Download chi_sim.traineddata from https://github.com/tesseract-ocr/tessdata
Place in: C:\Program Files\Tesseract-OCR\tessdata\

Basic Usage

Extract Text from Image

# Basic OCR (English)
tesseract image.png output.txt
Chinese OCR
tesseract image.png stdout -l chi_sim
Chinese + English OCR
tesseract image.png stdout -l chi_sim+eng
Specify output format
tesseract image.png output -l chi_sim+eng pdf txt

Common Patterns for Financial Analysis

# Extract text from financial announcement images
tesseract announcement.png stdout -l chi_sim+eng | grep -E "公司|股份|增长|利润"
Process multiple images
for img in *.png; do
    echo "=== $img ==="
    tesseract "$img" stdout -l chi_sim+eng
done
Save OCR results
tesseract financial_chart.png financial_analysis.txt -l chi_sim+eng

Integration with OpenClaw

Example: Process Telegram Image Messages

# When receiving image messages via Telegram
1. Image is automatically downloaded to media directory
2. Use OCR to extract text
3. Analyze extracted content
Find latest image
latest_img=$(ls -t "$HOME/.openclaw/media/inbound/"*.png | head -1)
Extract text
tesseract "$latest_img" stdout -l chi_sim+eng
Analyze for specific patterns (company names, financial data)
tesseract "$latest_img" stdout -l chi_sim+eng | grep -oE "#[^ ]+|【[^】]+】"

Example: Financial Announcement Analysis

#!/bin/bash
analyze_financial_image.sh
IMAGE="$1"
OUTPUT="analysis_$(date +%Y%m%d_%H%M%S).txt"
echo "=== OCR Analysis Report ===" > "$OUTPUT"
echo "Image: $IMAGE" >> "$OUTPUT"
echo "Time: $(date)" >> "$OUTPUT"
echo "" >> "$OUTPUT"
Extract text
echo "=== Extracted Text ===" >> "$OUTPUT"
tesseract "$IMAGE" stdout -l chi_sim+eng >> "$OUTPUT"
echo "" >> "$OUTPUT"
echo "=== Key Information ===" >> "$OUTPUT"
Extract company names
echo "Company Names:" >> "$OUTPUT"
tesseract "$IMAGE" stdout -l chi_sim+eng | grep -oE "[A-Za-z0-9]+股份|[A-Za-z0-9]+科技|[A-Za-z0-9]+集团" | sort -u >> "$OUTPUT"
Extract stock codes
echo "" >> "$OUTPUT"
echo "Stock Codes:" >> "$OUTPUT"
tesseract "$IMAGE" stdout -l chi_sim+eng | grep -oE "[0-9]{6}\.[A-Z]{2,4}" | sort -u >> "$OUTPUT"
Extract financial metrics
echo "" >> "$OUTPUT"
echo "Financial Metrics:" >> "$OUTPUT"
tesseract "$IMAGE" stdout -l chi_sim+eng | grep -oE "同比增长[0-9.]+%|利润[0-9.]+亿元|增长[0-9.]+%" | sort -u >> "$OUTPUT"
echo "Analysis saved to: $OUTPUT"

Advanced Usage

Multiple Language Support

# Chinese Simplified
tesseract image.png stdout -l chi_sim
Chinese Traditional
tesseract image.png stdout -l chi_tra
Japanese
tesseract image.png stdout -l jpn
Korean
tesseract image.png stdout -l kor
Multiple languages
tesseract image.png stdout -l chi_sim+eng+jpn

Image Preprocessing (Improve Accuracy)

# Convert to grayscale (using ImageMagick)
convert image.png -grayscale Rec709Luma grayscale.png
tesseract grayscale.png stdout -l chi_sim+eng
Increase contrast
convert image.png -contrast -contrast enhanced.png
tesseract enhanced.png stdout -l chi_sim+eng
Remove noise
convert image.png -despeckle denoised.png
tesseract denoised.png stdout -l chi_sim+eng

Batch Processing

# Process all PNG images in directory
for img in *.png; do
    base=$(basename "$img" .png)
    tesseract "$img" "output_${base}.txt" -l chi_sim+eng
    echo "Processed: $img -> output_${base}.txt"
done
Process with parallel (if available)
find . -name "*.png" -print0 | parallel -0 tesseract {} {.}.txt -l chi_sim+eng

Common Use Cases

1. Financial Announcements ("公告全知道")

# Extract key information from financial announcements
tesseract announcement.png stdout -l chi_sim+eng | \
    grep -A2 -B2 -E "公司|股份|增长|利润|合同|中标|收购"
Find company mentions
tesseract announcement.png stdout -l chi_sim+eng | \
    grep -oE "#[^ ]+|【[^】]+】|([A-Za-z0-9\u4e00-\u9fa5]+股份)"

2. Stock Charts and Tables

# Extract stock data from charts
tesseract stock_chart.png stdout -l eng | \
    grep -E "[0-9]+\.[0-9]+|[0-9]+%"
Process tabular data
tesseract table.png stdout -l chi_sim+eng | \
    awk 'BEGIN {FS="[[:space:]]{2,}"} {for(i=1;i<=NF;i++) printf "|%s", $i; print "|"}'

3. Document Screenshots

# Extract structured document content
tesseract document.png stdout -l chi_sim+eng | \
    sed -n '/^[0-9]\+\./p'  # Extract numbered items
Extract headings
tesseract document.png stdout -l chi_sim+eng | \
    grep -E "^#|^【|^（"

Troubleshooting

Common Issues

1. Poor OCR accuracy - Preprocess images (grayscale, contrast enhancement) - Use appropriate language packs - Ensure image resolution is sufficient (300 DPI recommended)

2. Missing Chinese characters - Verify chi_sim.traineddata is installed - Use -l chi_sim+eng for mixed content - Check image quality and font clarity

3. Tesseract not found - Install Tesseract via package manager - Add Tesseract to PATH environment variable - Verify installation with tesseract --version

Improving Accuracy

# Use custom configuration
tesseract image.png stdout -l chi_sim+eng --psm 6  # Assume uniform block of text
tesseract image.png stdout -l chi_sim+eng --psm 11  # Sparse text
PSM modes:
3 = Fully automatic page segmentation, but no OSD (default)
6 = Assume a single uniform block of text
11 = Sparse text. Find as much text as possible in no particular order
12 = Sparse text with OSD
Use OEM (OCR Engine Mode)
tesseract image.png stdout -l chi_sim+eng --oem 1  # LSTM only
tesseract image.png stdout -l chi_sim+eng --oem 2  # Legacy + LSTM
tesseract image.png stdout -l chi_sim+eng --oem 3  # Default

Performance Tips

Integration Examples

With Python Scripts

import subprocess
import re
def ocr_image(image_path, lang='chi_sim+eng'):
    """Extract text from image using Tesseract"""
    result = subprocess.run(
        ['tesseract', image_path, 'stdout', '-l', lang],
        capture_output=True,
        text=True,
        encoding='utf-8'
    )
    return result.stdout
Example usage
text = ocr_image('announcement.png')
companies = re.findall(r'#(\S+)', text)
print(f"Found companies: {companies}")

With Shell Scripts

#!/bin/bash
analyze_financial_images.sh
analyze_image() {
    local img="$1"
    echo "Analyzing: $img"
    
    # Extract text
    text=$(tesseract "$img" stdout -l chi_sim+eng)
    
    # Extract key information
    echo "=== Summary ==="
    echo "Companies: $(echo "$text" | grep -oE '#[^ ]+' | tr '\n' ' ')"
    echo "Stock Codes: $(echo "$text" | grep -oE '[0-9]{6}\.[A-Z]{2,4}' | tr '\n' ' ')"
    echo "Financial Terms: $(echo "$text" | grep -oE '同比增长|利润|增长|合同' | sort -u | tr '\n' ' ')"
}
Process all images
for img in "$@"; do
    analyze_image "$img"
    echo ""
done

Notes

# Verify Tesseract installation
tesseract --version
Install language packs if needed (for Chinese)
Windows: Download chi_sim.traineddata from https://github.com/tesseract-ocr/tessdata
Place in: C:\Program Files\Tesseract-OCR\tessdata\