layout-analyzer（布局分析器）

Layout Analyzer Skill

Overview

This skill enables document layout analysis using surya - an advanced document understanding system. Detect text blocks, tables, figures, headings, and determine reading order in complex documents.

How to Use

1. Provide the document image or PDF
2. Specify what layout elements to detect
3. I'll analyze the structure and return detected regions

Example prompts:

• "Analyze the layout of this document page"
• "Detect all tables and text blocks in this image"
• "Determine the reading order for this PDF page"
• "Find headings and paragraphs in this document"

Domain Knowledge

surya Fundamentals

from surya.detection import DetectionPredictor
from surya.layout import LayoutPredictor
from surya.reading_order import ReadingOrderPredictor
from PIL import Image

# Load image
image = Image.open("document.png")

# Detect layout elements
layout_predictor = LayoutPredictor()
layout_result = layout_predictor([image])

Layout Element Types

Text Detection

from surya.detection import DetectionPredictor
from PIL import Image

# Initialize detector
detector = DetectionPredictor()

# Load image
image = Image.open("document.png")

# Detect text regions
results = detector([image])

# Access results
for page_result in results:
    for bbox in page_result.bboxes:
        print(f"Text region: {bbox.bbox}")
        print(f"Confidence: {bbox.confidence}")

Layout Analysis

from surya.layout import LayoutPredictor
from PIL import Image

# Initialize layout predictor
layout_predictor = LayoutPredictor()

# Analyze layout
image = Image.open("document.png")
layout_results = layout_predictor([image])

# Process results
for page_result in layout_results:
    for element in page_result.bboxes:
        print(f"Type: {element.label}")
        print(f"Bbox: {element.bbox}")
        print(f"Confidence: {element.confidence}")

Reading Order Detection

from surya.reading_order import ReadingOrderPredictor
from surya.layout import LayoutPredictor
from PIL import Image

# Get layout first
layout_predictor = LayoutPredictor()
image = Image.open("document.png")
layout_results = layout_predictor([image])

# Determine reading order
reading_order_predictor = ReadingOrderPredictor()
order_results = reading_order_predictor([image], layout_results)

# Access ordered elements
for page_result in order_results:
    for i, element in enumerate(page_result.ordered_bboxes):
        print(f"{i+1}. {element.label}: {element.bbox}")

OCR with Layout

from surya.ocr import OCRPredictor
from surya.layout import LayoutPredictor
from PIL import Image

# Initialize predictors
ocr_predictor = OCRPredictor()
layout_predictor = LayoutPredictor()

# Load image
image = Image.open("document.png")

# Get layout
layout_results = layout_predictor([image])

# Run OCR
ocr_results = ocr_predictor([image])

# Combine results
for layout, ocr in zip(layout_results, ocr_results):
    for layout_elem in layout.bboxes:
        print(f"Element: {layout_elem.label}")

        # Find OCR text within this layout element
        for text_line in ocr.text_lines:
            if boxes_overlap(layout_elem.bbox, text_line.bbox):
                print(f"  Text: {text_line.text}")

Processing PDFs

from surya.layout import LayoutPredictor
from pdf2image import convert_from_path

def analyze_pdf_layout(pdf_path):
    """Analyze layout of all pages in PDF."""

    # Convert PDF to images
    images = convert_from_path(pdf_path)

    # Initialize predictor
    layout_predictor = LayoutPredictor()

    # Analyze all pages
    results = layout_predictor(images)

    document_structure = []

    for page_num, page_result in enumerate(results):
        page_elements = []

        for element in page_result.bboxes:
            page_elements.append({
                'type': element.label,
                'bbox': element.bbox,
                'confidence': element.confidence
            })

        document_structure.append({
            'page': page_num + 1,
            'elements': page_elements
        })

    return document_structure

structure = analyze_pdf_layout("document.pdf")

Visualization

from surya.layout import LayoutPredictor
from PIL import Image, ImageDraw, ImageFont

def visualize_layout(image_path, output_path):
    """Visualize detected layout elements."""

    image = Image.open(image_path)
    layout_predictor = LayoutPredictor()
    results = layout_predictor([image])

    # Create drawing context
    draw = ImageDraw.Draw(image)

    # Color mapping for element types
    colors = {
        'Text': 'blue',
        'Title': 'red',
        'Table': 'green',
        'Figure': 'purple',
        'Section-header': 'orange',
        'List-item': 'cyan',
    }

    for element in results[0].bboxes:
        bbox = element.bbox
        color = colors.get(element.label, 'gray')

        # Draw rectangle
        draw.rectangle(bbox, outline=color, width=2)

        # Add label
        draw.text((bbox[0], bbox[1] - 15),
                  f"{element.label} ({element.confidence:.2f})",
                  fill=color)

    image.save(output_path)
    return output_path

Best Practices

1. Use High-Quality Images: 150+ DPI for best results
2. Preprocess if Needed: Deskew rotated documents
3. Validate Results: Check confidence scores
4. Handle Multi-page: Process pages individually
5. Combine with OCR: Get text within detected regions

Common Patterns

Document Structure Extraction

def extract_document_structure(image_path):
    """Extract hierarchical document structure."""

    from surya.layout import LayoutPredictor
    from surya.reading_order import ReadingOrderPredictor

    image = Image.open(image_path)

    # Get layout
    layout_predictor = LayoutPredictor()
    layout_results = layout_predictor([image])

    # Get reading order
    order_predictor = ReadingOrderPredictor()
    order_results = order_predictor([image], layout_results)

    structure = {
        'title': None,
        'sections': [],
        'tables': [],
        'figures': []
    }

    current_section = None

    for element in order_results[0].ordered_bboxes:
        if element.label == 'Title':
            structure['title'] = element
        elif element.label == 'Section-header':
            current_section = {'header': element, 'content': []}
            structure['sections'].append(current_section)
        elif element.label == 'Table':
            structure['tables'].append(element)
        elif element.label == 'Figure':
            structure['figures'].append(element)
        elif current_section and element.label in ['Text', 'List-item']:
            current_section['content'].append(element)

    return structure

Table Region Extraction

def extract_table_regions(image_path):
    """Extract table regions from document."""

    from surya.layout import LayoutPredictor

    image = Image.open(image_path)
    layout_predictor = LayoutPredictor()
    results = layout_predictor([image])

    tables = []

    for element in results[0].bboxes:
        if element.label == 'Table':
            bbox = element.bbox

            # Crop table region
            table_image = image.crop(bbox)

            tables.append({
                'bbox': bbox,
                'image': table_image,
                'confidence': element.confidence
            })

    return tables

Examples

Example 1: Academic Paper Analysis

from surya.layout import LayoutPredictor
from surya.reading_order import ReadingOrderPredictor
from pdf2image import convert_from_path

def analyze_academic_paper(pdf_path):
    """Analyze structure of academic paper."""

    images = convert_from_path(pdf_path)

    layout_predictor = LayoutPredictor()
    order_predictor = ReadingOrderPredictor()

    paper_structure = {
        'pages': [],
        'element_counts': {
            'Title': 0,
            'Section-header': 0,
            'Text': 0,
            'Table': 0,
            'Figure': 0,
            'Formula': 0,
            'Footnote': 0
        }
    }

    layout_results = layout_predictor(images)
    order_results = order_predictor(images, layout_results)

    for page_num, (layout, order) in enumerate(zip(layout_results, order_results)):
        page_structure = {
            'page': page_num + 1,
            'elements': []
        }

        for element in order.ordered_bboxes:
            page_structure['elements'].append({
                'type': element.label,
                'bbox': element.bbox,
                'order': element.position
            })

            # Count element types
            if element.label in paper_structure['element_counts']:
                paper_structure['element_counts'][element.label] += 1

        paper_structure['pages'].append(page_structure)

    return paper_structure

paper = analyze_academic_paper('research_paper.pdf')
print(f"Total tables: {paper['element_counts']['Table']}")
print(f"Total figures: {paper['element_counts']['Figure']}")

Example 2: Form Field Detection

from surya.layout import LayoutPredictor
from PIL import Image

def detect_form_fields(image_path):
    """Detect form fields and labels."""

    image = Image.open(image_path)

    layout_predictor = LayoutPredictor()
    results = layout_predictor([image])

    form_fields = []

    for element in results[0].bboxes:
        # Look for text elements that might be labels
        if element.label == 'Text':
            # Check if there's a box/line nearby (potential input field)
            form_fields.append({
                'type': 'potential_label',
                'bbox': element.bbox,
                'confidence': element.confidence
            })

    return form_fields

fields = detect_form_fields('form.png')
print(f"Found {len(fields)} potential form elements")

Example 3: Multi-column Article

from surya.layout import LayoutPredictor
from surya.reading_order import ReadingOrderPredictor
from PIL import Image

def process_multicolumn_article(image_path):
    """Process multi-column article layout."""

    image = Image.open(image_path)

    layout_predictor = LayoutPredictor()
    order_predictor = ReadingOrderPredictor()

    layout_results = layout_predictor([image])
    order_results = order_predictor([image], layout_results)

    # Group elements by column
    image_width = image.width
    column_threshold = image_width / 2

    columns = {
        'left': [],
        'right': [],
        'full_width': []
    }

    for element in order_results[0].ordered_bboxes:
        bbox = element.bbox
        element_center = (bbox[0] + bbox[2]) / 2
        element_width = bbox[2] - bbox[0]

        # Determine column
        if element_width > column_threshold * 1.5:
            columns['full_width'].append(element)
        elif element_center < column_threshold:
            columns['left'].append(element)
        else:
            columns['right'].append(element)

    return {
        'layout': 'multi-column',
        'columns': columns,
        'reading_order': order_results[0].ordered_bboxes
    }

article = process_multicolumn_article('newspaper_page.png')
print(f"Left column: {len(article['columns']['left'])} elements")
print(f"Right column: {len(article['columns']['right'])} elements")

Limitations

• Handwritten layouts may be inaccurate
• Very small text regions may be missed
• Complex nested layouts challenging
• GPU recommended for batch processing
• Multi-language support varies

Installation

pip install surya-ocr

# For PDF processing
pip install pdf2image

Resources

• surya GitHub
• Model Documentation
• Examples