In the summer of 1990, Adobe co-founder Dr. John Warnock published a six-page white paper called The Camelot Project. He described the ubiquitous business challenge that existed at the time of exchanging documents between different applications and operating systems. Warnock wanted to create a way for anyone to easily capture, send, and view documents electronically from any application and on any machine.
Fast forward to 1992, and The Camelot Project evolved into what we now know as the Portable Document Format, or PDF. Today, businesses all over the world trust PDFs to store and share their most important documents. Although PDFs have revolutionized the way we work, extracting data from them remains a challenge. In this article, we'll be sharing tips and techniques to help you efficiently extract data from your PDFs. Let's dive in and discover the magic behind the paper-to-digital revolution.
What is PDF and why is it popular?
PDF is a file format that enables users to view, navigate, print, or forward an electronic image of a printed document. It is more than just an image of a document, as PDF files can embed type fonts and include interactive elements such as buttons for forms entry and for triggering sound or video. In creating PDF files, users can use tools such as Adobe Acrobat or other software that can save documents in PDF format.
To view saved PDF files, users can either use the full Adobe Acrobat program or a free program such as Adobe Reader. PDF files can also be viewed in most web browsers. A PDF file contains one or more page images, which users can zoom in on or out from and scroll backward and forward through.
There are many situations in which PDF files are desirable, such as when users want to preserve the original formatting of a document or create a document that cannot be easily edited. PDF files are useful for documents that need to preserve the original graphic appearance online or documents that are downloaded and printed, such as resumes, contracts, and application forms. PDFs also support embedding digital signatures in documents for authenticating the integrity of a digital document.
PDFs also have disadvantages. One major downside is that they aren’t easy to edit. If changes are needed after a document has been saved as a PDF, you must go back to the original program used to create it, make changes, and then save a new PDF image. Software such as Adobe Acrobat Pro does allow users to edit any PDF, but PDF editing tools are seldom free and introduce formatting complications for large adjustments. Another disadvantage is that some older versions of software cannot read PDFs, and recipients must have a PDF reader installed on their computer to open them.
PDFs can also pose security risks, as they can contain viruses. It's important to trust the source of any PDF files downloaded and to password-protect PDFs to restrict access to authorized users only.
PDF files can be converted to other file formats, such as Microsoft Word, Excel, or image formats like JPG. However, the format of the original document may not be perfectly preserved in the conversion process. Overall, PDFs are a useful tool for preserving document formatting and graphics, but they also have limitations and potential security risks.
Challenges of data extraction from PDFs
Extracting data from PDFs can be a challenging task, primarily due to the complexity of the format. PDF files are not structured in the same way as other document formats, such as Microsoft Word or HTML. Instead, they are essentially images of text that have been formatted to look like a document. As a result, it can be difficult to extract data from PDFs automatically.
One of the main challenges of extracting data from PDFs is the need for manual data entry. Since PDFs are not structured, it can be challenging to extract specific data points automatically. For example, if a PDF contains a table with data, it can be challenging to extract the data from the table and enter it into a spreadsheet or database automatically. In many cases, manual data entry is required to extract data accurately.
Another challenge of extracting data from PDFs is the risk of inaccuracies. When data is entered manually, there is always a risk of errors. For example, a data entry operator may accidentally enter the wrong number or misspell a name. These errors can lead to significant problems down the line, especially if the data is used for important decision-making.
Finally, the complexity of the PDF format itself can be a significant challenge. PDFs can contain a wide variety of formatting options, including text, images, tables, and charts. Extracting data from these different types of content can be difficult, especially if the PDF contains multiple types of content. In some cases, specialized software may be required to extract data from PDFs accurately.
Tips for extracting data from PDF
Preparing PDFs for data extraction can help to minimize the challenges and increase the accuracy of the extracted data. Here are some tips for optimizing PDFs for data extraction:
Optimize the PDF format: Converting a PDF file to a text-searchable format like PDF/A or HTML can help make it easier to extract data. For instance, let's say you have a PDF document containing a list of customer names, addresses, and phone numbers. Converting the PDF to PDF/A or HTML format can make it possible to extract this data using automated data extraction tools. This is because these formats allow for text search and make it easier for the software to locate and extract the relevant data points.
Use OCR technology: OCR technology can help to extract text from scanned PDFs, making it possible to extract data from documents that were not originally created digitally. OCR can extract data from PDFs produced from scanned handwritten notes or typed documents. This can save time and effort that would otherwise be spent on manual data entry.
Break down large files into smaller ones: Breaking down large PDF files into smaller ones can make it easier to extract data from them. For instance, a large PDF file containing data from multiple sources can be broken down down into smaller files by source or type of data, which can ease the data extraction process. This can also help to reduce errors that can occur when working with large, complex files.
Clean up the PDF: Removing unnecessary images, tables, or other content can help to simplify the PDF and make it easier to extract data. For example, a PDF document may contain a chart that is not relevant to the data extraction process. Removing this chart can help to simplify the PDF and make it easier to extract the relevant data points. This can also help to reduce the likelihood of errors that can occur when working with extraneous content.
Ensure text is clear and legible: Ensuring that the text is clear and legible can help to minimize inaccuracies in the extracted data. PDF documents containing text that is difficult to read due to poor image quality or low resolution are not amenable to accurate data extraction. Improving the quality of the PDF document by enhancing the image quality can help to make the text more legible, reducing the likelihood of errors when extracting data.
Standardize data formatting: Standardizing data formatting when creating the PDF can help to make it easier to extract data accurately. For instance, if a PDF document is to contain various dates, having all dates in a specific format (say, MM-DD-YYYY) can make it easier to extract the dates accurately. This can also help reduce the likelihood of errors that can occur when working with inconsistent data formatting.
Techniques for extracting data from PDFs
There are various methods by which data can be extracted from PDFs, and each comes with its own set of benefits and limitations.
Manual data entry is the most basic technique, where data is manually entered into a system from a PDF. This can be accomplished in two ways: the operator looks at the PDF and types the data into the target program, or the operator copy-pastes from the source PDF to the target program. This method is time-consuming, prone to errors, and costly. However, it sometimes feels like the only option when dealing with PDFs that are unstructured or contain complex data formats that cannot be easily extracted using automation tools. For example, manually entering data from an old PDF document that has no machine-readable text or structure.
Rather than rely on manual data entry, automated scripts can be developed using programming languages such as Python, Java, and Ruby to automate the process of extracting data. This method is faster and more accurate than manual data entry but requires expertise in programming and can be time-consuming to set up. Moreover, automated scripts can be useful in extracting data only from PDFs with a specific structure, but it can handle large volumes of data and can be tailored to specific needs. For example, scripts can be developed to extract invoice data from a large number of PDF files received from multiple vendors.
The next level of automatically extracting data from PDFs is the use of OCR. OCR technology can scan PDF documents and convert any machine-readable text into data that can be extracted. This technique can handle large volumes of data and can be useful in situations where the PDF documents are structured and contain machine-readable text. However, OCR technology is not perfect and can be prone to errors when dealing with complex data formats or when the PDF contains low-quality images or handwritten text. For example, OCR technology can be used to extract data from a large volume of scanned receipts received from customers.
PDF converters are software tools that allow you to convert various file formats to or vice versa— they use a variety of backend tools including scripts and OCR for their operation. However, these converters are largely used to facilitate the conversion of documents, images, spreadsheets, presentations, and other types of files into PDF format and rarely the other way around. There are many different types of PDF converters available, ranging from online tools that can be accessed via a web browser to standalone desktop applications that must be installed on your computer. Some PDF converters are free, while others require a fee to use.
Intelligent document processing (IDP) is the most advanced technique for extracting data from PDFs. IDP tools use a combination of OCR technology, machine learning algorithms, natural language processing, and humans-in-the-loop (HITL) to extract data from PDFs accurately and efficiently. IDP tools can handle unstructured PDFs and can extract data from complex data formats such as tables, graphs, and charts. This technique can be useful for automating data extraction from PDFs that contain a large volume of unstructured data or when the data needs to be extracted quickly and accurately. For example, IDP tools can be used to extract data from insurance claims, financial reports, or legal contracts.
Why is IDP better than OCR?
While OCR may be acceptable for automating data extraction from low volumes of PDFs that follow a similar structure, it becomes unwieldy and unreliable with larger volumes and variable document formats. IDP’s superiority over OCR arise from its following features.
Handling Unstructured Data: PDF documents often contain unstructured data like tables, graphs, and images in addition to text. OCR tools are designed to extract only text data, making it difficult to extract information from tables, graphs, or images. For example, a healthcare provider may receive medical records in PDF format that contain tables, images, and text. An OCR tool can only extract the text from the records, making it difficult to extract information from tables or images – absence of clarity can be a serious problem in healthcare as data is often the thread on which hangs life (or health, for a less morbid outlook). In contrast, an IDP tool can extract data from both structured and unstructured formats, making it easier to extract any data type accurately.
Automated Data Extraction: OCR tools can extract text from PDF documents, but they cannot automate the extraction of data or interpret the data's meaning. IDP tools can extract, classify, and interpret data from PDFs, making it easier to process and analyze data quickly. A financial institution, for example, may receive loan applications in PDF format. An OCR tool can only extract the text from the applications, while an IDP tool can extract data like income, employment history, and loan amount from the applications, making it easier to process and analyze the loan applications.
Improved Accuracy: OCR tools can sometimes produce inaccurate results, especially when dealing with complex documents, handwritten text, or documents with poor image quality. IDP tools, on the other hand, use advanced machine learning algorithms and natural language processing to improve the accuracy of data extraction. For example, an insurance company may receive claim forms in PDF format that contain handwritten text, making it difficult to extract data accurately using OCR. An IDP tool can use machine learning algorithms that can be trained on handwriting styles, making it easier to extract data accurately from claim forms.
Greater Flexibility: IDP tools offer greater flexibility than OCR tools, as they can handle multiple document formats beyond just PDF, such as Word, Excel, and emails. They can also be customized to handle specific document types or fields, making it easier to extract data from documents specific to an industry or business. Take the case of a logistics company that receives shipment invoices in multiple formats like PDF, Word, or email attachments. An IDP tool can extract data from all the formats, making it easier to process invoices from different sources. The IDP tool can also be customized to extract specific fields like shipment date, quantity, and price, making it easier to analyze invoice data.
Best practices to ensure the accuracy of extracted data
While IDP does promise and deliver fairly accurate data extraction, there is always a small risk of data errors. Here are some best practices that can ensure the accuracy of data extracted from PDFs.
Data Validation Rules: Data validation is the process of ensuring that the data entered into a system or database is accurate, consistent, and meets the required quality parameters. There are various methods to perform data validation, including validation by scripts and validation by programs. Validation by scripts involves writing code to compare data values and structure against defined rules to verify that all necessary information is within the required quality parameters. This method can be time-consuming, depending on the complexity and size of the data set being validated. Alternatively, many software programs can be used to perform data validation automatically. These programs are designed to understand defined rules and file structures, making the validation process straightforward. The ideal validation tool should allow validation to be built into every step of the workflow, without requiring an in-depth understanding of the underlying format.
Data Profiling: Data profiling is a process that involves analyzing and evaluating data to understand its quality, structure, and relationships. Applications that perform data profiling use techniques such as column profiling, cross-column profiling, and cross-table profiling. These profiling techniques can be categorized into three categories: structure discovery, content discovery, and relationship discovery. Structure discovery helps determine if the data is consistent and formatted correctly, while content discovery focuses on data quality and standardization. Relationship discovery identifies connections between different datasets. By using data profiling techniques, analysts can gain insights into data quality and structure, making it easier to identify potential issues and improve the accuracy and usefulness of the data.
Data Cleansing Techniques: Data cleansing techniques are essential because they help to eliminate errors and inconsistencies in the data. For instance, if you have a database of product SKUs, you may have entries that are misspelled or inconsistent in their formatting. Data cleansing techniques such as data standardization and normalization can correct these errors, ensuring that the data is consistent and accurate.
Manual Review: Manual review is essential because it allows you to identify and correct errors that may not be detected through automated processes. For example, if you have a database of financial transactions, manual review can help you to identify transactions that are incorrect or incomplete. This can include identifying missing information or incorrect entries that may not be caught through data validation rules or data profiling.
Iterative Process: An iterative process is essential because it allows you to refine the data preparation process over time. For example, if you identify a common error in the data during the first round of cleaning, you can add a data validation rule to prevent that error from occurring in future extractions. By iterating through the process of verifying and cleaning up extracted data, you can continually improve the quality and accuracy of the data.
Use cases for data extraction from PDFs
PDF data extraction is useful in many sectors and applications:
Once the data has been extracted from PDFs, it can be used for a wide range of data analysis applications.
Financial analysis: One of the most common uses of PDF data extraction is in financial analysis. Many financial statements, such as income statements and balance sheets, are stored as PDF files. Extracting the data from these files can help analysts analyze trends and make forecasts about a company's financial performance.
Sentiment analysis: PDF data extraction can also be used for sentiment analysis. Many documents, such as news articles and customer reviews, are stored in PDF format. By extracting the text from these documents, sentiment analysis algorithms can analyze the language used to determine whether the sentiment is positive, negative, or neutral.
Legal analysis: Legal documents such as contracts, court cases, and patents are often stored as PDF files. By extracting relevant data from these documents, legal professionals can analyze language patterns, identify key clauses or arguments, and extract metadata such as dates, parties, and jurisdictions. This can be useful for contract management, patent analysis, and legal research.
Healthcare analysis: Medical reports, research papers, and clinical trials are often published in PDF format. Extracting data from these files can help healthcare professionals and researchers analyze patient data, track disease outbreaks, and identify trends in medical research. For example, by extracting data from clinical trials, researchers can analyze the efficacy of drugs and treatments, identify potential side effects, and make recommendations for future research. Similarly, healthcare providers can extract data from medical records to analyze patient outcomes, track disease patterns, and identify areas for improvement in patient care.
Data extraction from PDFs can be a powerful tool for automating business operations. Here are a few ways in which it can be used:
Invoice processing: Many businesses receive invoices from suppliers and vendors in PDF format. Extracting data from these invoices can help automate the accounts payable process, reducing manual data entry and errors. By extracting information such as the vendor name, invoice date, and payment amount, the data can be automatically entered into the accounting system, triggering payment workflows and reducing the time it takes to process invoices.
Form processing: Many businesses use forms, such as job applications, customer surveys, and feedback forms, to collect data from customers and employees. By extracting data from these forms, businesses can automate data entry, reducing the time it takes to process the forms and improving accuracy. For example, by extracting data such as name, email, and phone number from job applications, businesses can automatically create candidate profiles in their applicant tracking system, streamlining the recruitment process.
Contract management: Many businesses use contracts to manage relationships with suppliers, customers, and partners. By extracting data from these contracts, businesses can automate contract management, reducing the time it takes to review and update contracts. By extracting information such as renewal dates, termination clauses, and payment terms, businesses can set up automated alerts for contract renewals and expirations, ensuring that contracts are managed effectively and efficiently.
Data extraction from PDFs can be a valuable tool for decision-making, as it provides access to structured data that can be analyzed to gain insights and inform strategic decisions. Here are a few ways in which it can help:
Market research: Many market research reports and industry analyses are published in PDF format. By extracting data from these reports, businesses can gain insights into market trends, customer behavior, and competitive landscapes. This data can be analyzed to inform strategic decisions, such as product development, marketing strategies, and market expansion.
Compliance reporting: Many regulatory filings, such as annual reports and tax returns, are stored in PDF format. By extracting data from these filings, businesses can ensure compliance with legal and regulatory requirements, and make informed decisions about tax planning and risk management.
Customer feedback analysis: Many customer feedback forms and surveys are collected in PDF format. By extracting data from these forms, businesses can analyze customer feedback, identify areas for improvement, and make strategic decisions about product development, customer service, and marketing strategies.
PDFs aren’t going anywhere anytime soon
Data is the driving force of businesses. Extracting data from PDFs can be a powerful tool for gaining insights and informing strategic decision-making. PDFs are a useful format for storing information, but extracting data from them is essential for unlocking their full potential.
Fortunately, AI tools are making it easier than ever to extract data from PDFs quickly and accurately. By using machine learning algorithms to recognize patterns and extract information from unstructured data, businesses can save time and reduce errors in data processing.
Looking ahead, it's clear that data extraction from PDFs will continue to play an increasingly important role in business operations. As businesses generate more and more data in PDF format, the ability to extract that data and turn it into actionable insights will be essential for staying competitive. Businesses that invest in AI-powered data extraction tools are poised to gain a significant advantage over those that don't.
Get Started on Your Automation Journey
Learn more by speaking to an unstructured data expert
How Are Large Language Models Reshaping Intelligent Document Processing?
Unlock the Power of AI in Invoice Matching
Save Your Business Time and Money with Intelligent Data Extraction
Automating PO Matching with AI
Efficiently Extracting Data from PDFs
Start processing 100% of complex documents.
Submit your information to get started with super.AI Intelligent Document Processing (IDP).