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:

• Extract: Load the PDF document

• Transform: Parse and structure the content

• Load: Save to CSV, Excel, or JSON

Quick Start

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

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

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

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

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

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

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

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

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

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

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

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

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