Portable Document Format (PDF) files have become the standard global format for sharing documents digitally across devices while preserving original formatting and layouts intact. However, PDF files can often pose challenges for efficiently extracting usable data sets compared to plaintext documents, spreadsheets or other formats more optimized for data analysis tasks.
This article explores the main methods both manual and automated for extracting actionable data out of PDF files. It also covers key best practices technology specialists should follow for optimizing PDF data harvesting and some persistent challenges around converting scanned documents.
Methods for Extracting Data from PDF Files
Contents
There are a few core approaches ranging from entirely manual techniques to leveraging software assistance that can be taken by data teams, analysts, or individuals to pull usable data sets, tables, and content out of PDF documents. Each one has one of a kind advantages and downsides.
Manual Data Extraction Techniques
The most basic way to obtain data from PDFs involves manual copying and pasting or retyping information selected by users. This puts all of the responsibility on human labor.
Copy and Paste the Text
A majority of common PDF viewers and reader programs allow individual strings of text spanning a few paragraphs to be manually selected using a cursor then directly copied and pasted into other documents or spreadsheets. Though limited in scope, this functionality allows for quick extraction of small, targeted data sets by trained users. Markup tools can assist in isolating relevant passages.
Manually Retype Content
For scanned or securely encrypted PDF files that explicitly block selecting and copying of text, users have little option but to manually retype and transcribe key information from the PDF documents into new formats word for word to capture the data. This is an extremely tedious and time intensive process, especially for longer documents.
Software-Assisted Data Extraction
Specialized PDF software solutions often have built-in functionality to enable bulk exporting of PDF data into other more usable formats. This automates part of the process.
Adobe Acrobat Exporting
The full Adobe Acrobat Pro DC version contains an “Export PDF” wizard that allows full PDF files to be converted into various document types including Microsoft Word, Excel and PowerPoint. This enables PDF data to be output into editable Office documents for further analysis. But re-formatting is sometimes lost.
Foxit PDF Reader Conversion
Similarly, Foxit’s PDF reading software contains a “Convert” module enabling one-click conversion of text, images, shapes and other content within PDF files into selectable and editable Microsoft Office file formats. This allows data usage without restrictions. But layouts break.
PopAi AI-Powered Data Extraction
PopAi is an emerging AI pdf reader specifically engineered to use advanced artificial intelligence called Optical Character Recognition (OCR) and machine learning algorithms to scan PDF documents then intelligently transform and extract only the most relevant data sets into cleanly formatted, editable Excel spreadsheets on demand. This automates manual efforts.
PDF File Conversion Tools
Dedicated PDF converter software exists to transform entire PDF files into alternative document formats better optimized for data analysis. These present tradeoffs.
Online PDF to Word Converters
A range of free cloud-based tools like PDF2Go, SmallPDF or Adobe’s own Export PDF online functionality allow uploading PDF files to then convert the contents into Word, Excel, PowerPoint and other documents while maintaining some formatting. Data can then be copied.
ABBYY FineReader OCR
ABBYY FineReader represents the gold standard in proprietary Optical Character Recognition and PDF conversion software with advanced AI capabilities to scan PDF documents retaining original formatting, and convert them into various editable formats like Microsoft Word, Excel and more depending on user needs. However, costs can be high for advanced options tailored to specific use cases.
Best Practices for Efficient Data Extraction
Based on the collective experience of data teams across various industries, below are 3 key best practices to follow for keeping PDF data extraction as efficient as possible.
Organize PDF Files Meticulously
Leveraging clear, consistent descriptive naming conventions, hierarchical folders by topic, selective tags and search metadata for stored PDF files allows more easily locating documents containing the relevant data needed at any given time later, especially at scale across large enterprises. Clean organization cuts down extraction time.
Choose Optimal Tools Aligned to Use Case
Rather than taking a one-size-fits-all approach, organizations should ideally select PDF reader and data extraction tools purpose-built to match their specific use cases, content types and data needs at scale. The right software minimizes manual overhead for recurring extraction.
Utilize Batch Processing Where Possible
Tools like PopAi natively support high-volume batch uploading of hundreds or thousands of PDF files-at-once for automated data identification and conversion instead of slow individual manual processing. This achieves immense time savings that positively impacts productivity.
Challenges in Converting PDF Files to Data
However, while methods and tools continue advancing, there still remains some key underlying challenges involved trying to reliably extract clean data from PDF documents automatically.
Scanned PDF Files
PDF documents created from scanning hard copies of paper printouts rather than natively digital sources present OCR and conversion difficulty depending on scan quality resolution. Lower quality scans impede accuracy.
Security Restrictions
Some PDF files are encrypted by authors or intentionally use added security restrictions explicitly blocking the copying/pasting and selection of text to prevent unauthorized usage, which severely hampers extracting data forcing manual workarounds.
Conclusion
In summary, across the key areas covered – methods, tools, best practices and lingering challenges – PDF data extraction continues to grow easier. Following the suggested guidelines and leveraging the newest innovations in artificial intelligence-powered software automation can save significant time and effort while optimizing results.
As AI research including natural language processing, computer vision and machine learning algorithms steadily advance year over year, purpose-built software solutions specialized specifically for unlocking clean, structured data sets from even highly complex PDF document types will likely proliferate. This presents a path to minimizing manual data extraction efforts at scale across industries.
