Pdf To Structured

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Extract structured data from construction PDFs. Convert specifications, BOMs, schedules, and reports from PDF to Excel/CSV/JSON. Use OCR for scanned documents and pdfplumber for native PDFs.

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About This Skill

# PDF to Structured Data Conversion
## Overview
Based on DDC methodology (Chapter 2.4), this skill transforms unstructured PDF documents into structured formats suitable for analysis and integration. Construction projects generate vast amounts of PDF documentation - specifications, BOMs, schedules, and reports - that need to be extracted and processed.
Book Reference: "Преобразование данных в структурированную форму" / "Data Transformation to Structured Form"
> "Преобразование данных из неструктурированной в структурированную форму — это и искусство, и наука. Этот процесс часто занимает значительную часть работы инженера по обработке данных."
> — DDC Book, Chapter 2.4
## ETL Process Overview
The conversion follows the ETL pattern:
1. Extract: Load the PDF document
2. Transform: Parse and structure the content
3. Load: Save to CSV, Excel, or JSON
## Quick Start
```python
import pdfplumber
import pandas as pd
# Extract table from PDF
with pdfplumber.open("construction_spec.pdf") as pdf:
page = pdf.pages[0]
table = page.extract_table()
df = pd.DataFrame(table[1:], columns=table[0])
df.to_excel("extracted_data.xlsx", index=False)
```
## Installation
```bash
# Core libraries
pip install pdfplumber pandas openpyxl
# For scanned PDFs (OCR)
pip install pytesseract pdf2image
# Also install Tesseract OCR: https://github.com/tesseract-ocr/tesseract
# For advanced PDF operations
pip install pypdf
```
## Native PDF Extraction (pdfplumber)
### Extract All Tables from PDF
```python
import pdfplumber
import pandas as pd
def extract_tables_from_pdf(pdf_path):
"""Extract all tables from a PDF file"""
all_tables = []
with pdfplumber.open(pdf_path) as pdf:
for page_num, page in enumerate(pdf.pages):
tables = page.extract_tables()
for table_num, table in enumerate(tables):
if table and len(table) > 1:
# First row as header
df = pd.DataFrame(table[1:], columns=table[0])
df['_page'] = page_num + 1
df['_table'] = table_num + 1
all_tables.append(df)
if all_tables:
return pd.concat(all_tables, ignore_index=True)
return pd.DataFrame()
# Usage
df = extract_tables_from_pdf("material_specification.pdf")
df.to_excel("materials.xlsx", index=False)
```
### Extract Text with Layout
```python
import pdfplumber
def extract_text_with_layout(pdf_path):
"""Extract text preserving layout structure"""
full_text = []
with pdfplumber.open(pdf_path) as pdf:
for page in pdf.pages:
text = page.extract_text()
if text:
full_text.append(text)
return "\n\n--- Page Break ---\n\n".join(full_text)
# Usage
text = extract_text_with_layout("project_report.pdf")
with open("report_text.txt", "w", encoding="utf-8") as f:
f.write(text)
```
### Extract Specific Table by Position
```python
import pdfplumber
import pandas as pd
def extract_table_from_area(pdf_path, page_num, bbox):
"""
Extract table from specific area on page
Args:
pdf_path: Path to PDF file
page_num: Page number (0-indexed)
bbox: Bounding box (x0, top, x1, bottom) in points
"""
with pdfplumber.open(pdf_path) as pdf:
page = pdf.pages[page_num]
cropped = page.within_bbox(bbox)
table = cropped.extract_table()
if table:
return pd.DataFrame(table[1:], columns=table[0])
return pd.DataFrame()
# Usage - extract table from specific area
# bbox format: (left, top, right, bottom) in points (1 inch = 72 points)
df = extract_table_from_area("drawing.pdf", 0, (50, 100, 550, 400))
```
## Scanned PDF Processing (OCR)
### Extract Text from Scanned PDF
```python
import pytesseract
from pdf2image import convert_from_path
import pandas as pd
def ocr_scanned_pdf(pdf_path, language='eng'):
"""
Extract text from scanned PDF using OCR
Args:
pdf_path: Path to scanned PDF
language: Tesseract language code (eng, deu, rus, etc.)
"""
# Convert PDF pages to images
images = convert_from_path(pdf_path, dpi=300)
extracted_text = []
for i, image in enumerate(images):
text = pytesseract.image_to_string(image, lang=language)
extracted_text.append({
'page': i + 1,
'text': text
})
return pd.DataFrame(extracted_text)
# Usage
df = ocr_scanned_pdf("scanned_specification.pdf", language='eng')
df.to_csv("ocr_results.csv", index=False)
```
### OCR Table Extraction
```python
import pytesseract
from pdf2image import convert_from_path
import pandas as pd
import cv2
import numpy as np
def ocr_table_from_scanned_pdf(pdf_path, page_num=0):
"""Extract table from scanned PDF using OCR with table detection"""
# Convert specific page to image
images = convert_from_path(pdf_path, first_page=page_num+1,
last_page=page_num+1, dpi=300)
image = np.array(images[0])
# Convert to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
# Apply thresholding
_, binary = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY_INV)
# Extract text with table structure
custom_config = r'--oem 3 --psm 6'
text = pytesseract.image_to_string(gray, config=custom_config)
# Parse text into table structure
lines = text.strip().split('\n')
data = [line.split() for line in lines if line.strip()]
if data:
# Assume first row is header
df = pd.DataFrame(data[1:], columns=data[0] if len(data[0]) > 0 else None)
return df
return pd.DataFrame()
# Usage
df = ocr_table_from_scanned_pdf("scanned_bom.pdf")
print(df)
```
## Construction-Specific Extractions
### Bill of Materials (BOM) Extraction
```python
import pdfplumber
import pandas as pd
import re
def extract_bom_from_pdf(pdf_path):
"""Extract Bill of Materials from construction PDF"""
all_items = []
with pdfplumber.open(pdf_path) as pdf:
for page in pdf.pages:
tables = page.extract_tables()
for table in tables:
if not table or len(table) < 2:
continue
# Find header row (look for common BOM headers)
header_keywords = ['item', 'description', 'quantity', 'unit', 'material']
for i, row in enumerate(table):
if row and any(keyword in str(row).lower() for keyword in header_keywords):
# Found header, process remaining rows
headers = [str(h).strip() for h in row]
for data_row in table[i+1:]:
if data_row and any(cell for cell in data_row if cell):
item = dict(zip(headers, data_row))
all_items.append(item)
break
return pd.DataFrame(all_items)
# Usage
bom = extract_bom_from_pdf("project_bom.pdf")
bom.to_excel("bom_extracted.xlsx", index=False)
```
### Project Schedule Extraction
```python
import pdfplumber
import pandas as pd
from datetime import datetime
def extract_schedule_from_pdf(pdf_path):
"""Extract project schedule/gantt data from PDF"""
with pdfplumber.open(pdf_path) as pdf:
all_tasks = []
for page in pdf.pages:
tables = page.extract_tables()
for table in tables:
if not table:
continue
# Look for schedule-like table
headers = table[0] if table else []
# Check if it looks like a schedule
schedule_keywords = ['task', 'activity', 'start', 'end', 'duration']
if any(kw in str(headers).lower() for kw in schedule_keywords):
for row in table[1:]:
if row and any(cell for cell in row if cell):
task = dict(zip(headers, row))
all_tasks.append(task)
df = pd.DataFrame(all_tasks)
# Try to parse dates
date_columns = ['Start', 'End', 'Start Date', 'End Date', 'Finish']
for col in date_columns:
if col in df.columns:
df[col] = pd.to_datetime(df[col], errors='coerce')
return df
# Usage
schedule = extract_schedule_from_pdf("project_schedule.pdf")
print(schedule)
```
### Specification Parsing
```python
import pdfplumber
import pandas as pd
import re
def parse_specification_pdf(pdf_path):
"""Parse construction specification document"""
specs = []
with pdfplumber.open(pdf_path) as pdf:
full_text = ""
for page in pdf.pages:
text = page.extract_text()
if text:
full_text += text + "\n"
# Parse sections (common spec format)
section_pattern = r'(\d+\.\d+(?:\.\d+)?)\s+([A-Z][^\n]+)'
sections = re.findall(section_pattern, full_text)
for num, title in sections:
specs.append({
'section_number': num,
'title': title.strip(),
'level': len(num.split('.'))
})
return pd.DataFrame(specs)
# Usage
specs = parse_specification_pdf("technical_spec.pdf")
print(specs)
```
## Batch Processing
### Process Multiple PDFs
```python
import pdfplumber
import pandas as pd
from pathlib import Path
def batch_extract_tables(folder_path, output_folder):
"""Process all PDFs in folder and extract tables"""
pdf_files = Path(folder_path).glob("*.pdf")
results = []
for pdf_path in pdf_files:
print(f"Processing: {pdf_path.name}")
try:
with pdfplumber.open(pdf_path) as pdf:
for page_num, page in enumerate(pdf.pages):
tables = page.extract_tables()
for table_num, table in enumerate(tables):
if table and len(table) > 1:
df = pd.DataFrame(table[1:], columns=table[0])
df['_source_file'] = pdf_path.name
df['_page'] = page_num + 1
# Save individual table
output_name = f"{pdf_path.stem}_p{page_num+1}_t{table_num+1}.xlsx"
df.to_excel(Path(output_folder) / output_name, index=False)
results.append(df)
except Exception as e:
print(f"Error processing {pdf_path.name}: {e}")
# Combined output
if results:
combined = pd.concat(results, ignore_index=True)
combined.to_excel(Path(output_folder) / "all_tables.xlsx", index=False)
return len(results)
# Usage
count = batch_extract_tables("./pdf_documents/", "./extracted/")
print(f"Extracted {count} tables")
```
## Data Cleaning After Extraction
```python
import pandas as pd
def clean_extracted_data(df):
"""Clean common issues in PDF-extracted data"""
# Remove completely empty rows
df = df.dropna(how='all')
# Strip whitespace from string columns
for col in df.select_dtypes(include=['object']).columns:
df[col] = df[col].str.strip()
# Remove rows where all cells are empty strings
df = df[df.apply(lambda row: any(cell != '' for cell in row), axis=1)]
# Convert numeric columns
for col in df.columns:
# Try to convert to numeric
numeric_series = pd.to_numeric(df[col], errors='coerce')
if numeric_series.notna().sum() > len(df) * 0.5: # More than 50% numeric
df[col] = numeric_series
return df
# Usage
df = extract_tables_from_pdf("document.pdf")
df_clean = clean_extracted_data(df)
df_clean.to_excel("clean_data.xlsx", index=False)
```
## Export Options
```python
import pandas as pd
import json
def export_to_multiple_formats(df, base_name):
"""Export DataFrame to multiple formats"""
# Excel
df.to_excel(f"{base_name}.xlsx", index=False)
# CSV
df.to_csv(f"{base_name}.csv", index=False, encoding='utf-8-sig')
# JSON
df.to_json(f"{base_name}.json", orient='records', indent=2)
# JSON Lines (for large datasets)
df.to_json(f"{base_name}.jsonl", orient='records', lines=True)
# Usage
df = extract_tables_from_pdf("document.pdf")
export_to_multiple_formats(df, "extracted_data")
```
## Quick Reference
| Task | Tool | Code |
|------|------|------|
| Extract table | pdfplumber | `page.extract_table()` |
| Extract text | pdfplumber | `page.extract_text()` |
| OCR scanned | pytesseract | `pytesseract.image_to_string(image)` |
| Merge PDFs | pypdf | `writer.add_page(page)` |
| Convert to image | pdf2image | `convert_from_path(pdf)` |
## Troubleshooting
| Issue | Solution |
|-------|----------|
| Table not detected | Try adjusting table settings: `page.extract_table(table_settings={})` |
| Wrong column alignment | Use visual debugging: `page.to_image().draw_rects()` |
| OCR quality poor | Increase DPI, preprocess image, use correct language |
| Memory issues | Process pages one at a time, close PDF after processing |
## Resources
Book: "Data-Driven Construction" by Artem Boiko, Chapter 2.4
Website: https://datadrivenconstruction.io
pdfplumber Docs: https://github.com/jsvine/pdfplumber
Tesseract OCR: https://github.com/tesseract-ocr/tesseract
## Next Steps
See `image-to-data` for image processing
See `cad-to-data` for CAD/BIM data extraction
See `etl-pipeline` for automated processing workflows
See `data-quality-check` for validating extracted data

Use Cases

Extract text from scanned PDF documents using OCR recognition
Convert PDF files to other formats (Word, Excel, images) and vice versa
Extract text, images, and structured data from PDF files
Recognize and extract tables from PDF documents
Convert PDF pages to image formats (PNG, JPG) for preview or sharing

Pros & Cons

Pros

+ Well-adopted with 2,857+ downloads showing reliable real-world usage
+ Community-endorsed with 7 stars on ClawHub
+ Generates professional-quality output suitable for direct use
+ Structured workflow ensures consistent documentation quality

Cons

- Requires installing external dependencies before use
- Generated content may need manual review and editing for accuracy
- Template-based approach may not suit highly specialized document formats

Frequently Asked Questions

What does Pdf To Structured do?

Extract structured data from construction PDFs. Convert specifications, BOMs, schedules, and reports from PDF to Excel/CSV/JSON. Use OCR for scanned documents and pdfplumber for native PDFs.

What platforms support Pdf To Structured?

Pdf To Structured is available on Claude Code, OpenClaw.

What are the use cases for Pdf To Structured?

Extract text from scanned PDF documents using OCR recognition. Convert PDF files to other formats (Word, Excel, images) and vice versa. Extract text, images, and structured data from PDF files.

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