image-ocr

Files (1)

• SKILL.md

SKILL.md (first lines)

# Image OCR Skill

## Purpose
This skill enables accurate text extraction from image files (JPG, PNG, etc.) using Tesseract OCR via the `pytesseract` Python library. It is suitable for scanned documents, screenshots, photos of text, receipts, forms, and other visual content containing text.

## When to Use
- Extracting text from scanned documents or photos
- Reading text from screenshots or image captures
- Processing batch image files that contain textual information
- Converting visual documents to machine-readable text
- Extracting structured data from forms, receipts, or tables in images

## Required Libraries

The following Python libraries are required:

```python
import pytesseract
from PIL import Image
import json
import os
```

## Input Requirements
- **File formats**: JPG, JPEG, PNG, WEBP
- **Image quality**: Minimum 300 DPI recommended for printed text; clear and legible text
- **File size**: Under 5MB per image (resize if necessary)
- **Text language**: Specify if non-English to improve accuracy

## Output Schema
All extracted content must be returned as valid JSON conforming to this schema:

```json
{
  "success": true,
  "filename": "example.jpg",
  "extracted_text": "Full raw text extracted from the image...",
  "confidence": "high|medium|low",
  "metadata": {
    "language_detected": "en",
    "text_regions": 3,
    "has_tables": false,
    "has_handwriting": false
  },
  "warnings": [
    "Text partially obscured in bottom-right corner",
    "Low contrast detected in header section"
  ]
}
```


### Field Descriptions

- `success`: Boolean indicating whether text extraction completed
- `filename`: Original image filename
- `extracted_text`: Complete text content in reading order (top-to-bottom, left-to-right)
- `confidence`: Overall OCR confidence level based on image quality and text clarity
- `metadata.language_detected`: ISO 639-1 language code
- `metadata.text_regions`: Number of distinct text blocks identified
- `metadata.has_tables`: Whether tabular data structures were detected
- `metadata.has_handwriting`: Whether handwritten text was detected
- `warnings`: Array of quality issues or potential errors


## Code Examples

### Basic OCR Extraction

```python
import pytesseract
from PIL import Image

def extract_text_from_image(image_path):
    """Extract text from a single image using Tesseract OCR."""
    img = Image.open(image_path)
    text = pytesseract.image_to_string(img)
    return text.strip()
```

### OCR with Confidence Data

```python
import pytesseract
from PIL import Image

def extract_with_confidence(image_path):
    """Extract text with per-word confidence scores."""
    img = Image.open(image_path)

    # Get detailed OCR data including confidence
    data = pytesseract.image_to_data(img, output_type=pytesseract.Output.DICT)

    words = []
    confidences = []

    for i, word in enumerate(data['text']):
        if word.strip():  # Skip empty strings
            words.append(word)
            confidences.append(data['conf'][i])

    # Calculate average confidence
    avg_confidence = sum(c for c in confidences if c > 0) / len([c for c in confidences if c > 0]) if confidences else 0

    return {
        'text': ' '.join(words),
        'average_confidence': avg_confidence,
        'word_count': len(words)
    }
```

### Full OCR with JSON Output

```python
import pytesseract
from PIL import Image
import json
import os

From benchflow-ai/skillsbench, Apache-2.0.

More from benchflow-ai/skillsbench