What Is PDFMiner And Should You Use It - How To Extract Data From PDFs

What Is PDFMiner And Should You Use It – How To Extract Data From PDFs

PDF files are one of the most popular file formats today. Because they can preserve the visual layout of documents and are compatible with a wide range of devices and operating systems, PDFs are used for everything from business forms and educational material to creative designs.

However, PDF files also present multiple challenges when it comes to extracting data:

PDFs do not have a single standardized layout, and it may be difficult or impossible to retain the original formatting during extraction.
The lack of metadata, such as headings and paragraphs, can make it harder to understand the relationships between various elements.
Advanced elements, such as footnotes, sidebars, and multi-column tables, may not be easily converted into pure text.
Scanned PDFs may suffer from quality issues, resulting in incorrect or missing characters in the extracted data.

The good news is that Python text extraction tools such as PDFMiner can help users parse and work with data in PDF files. PDFMiner’s focus on text extraction and layout preservation sets it apart from other libraries.

So, what is PDFMiner, and how does it work? Below, we’ll look at the tool’s key features and use cases, as well as some pitfalls to be aware of when doing PDF text extraction in Python.

What Is PDFMiner?

PDFMiner is a Python package for extracting text, metadata, and other types of information from PDF files. PDFMiner supports Python 3.6 and above.

The key features of PDFMiner include:

Extracting detailed information about text locations, fonts, and other layout data
Automatically performing layout analysis of PDF files
Specifying granular options for text extraction
Reading encrypted and restricted PDF files (password required)
Converting PDF files into other formats like HTML and XML

PDFMiner’s extensive functionality makes it suitable for many different applications; however, it is likely a better fit for advanced use cases rather than simple PDF manipulation. If you’re looking to solve a more straightforward problem, it might be worth investigating some of the alternatives to PDFMiner.

Core Functionalities of PDFMiner

PDFMiner comes with a predefined set of key modules, each with a different purpose. These modules can be accessed either via the command line or by importing them into a Python project.

The PDFParser module is designed for low-level parsing of PDF documents. For example, the following Python code takes in the name of a PDF file and creates a PDFParser object:

parser = PDFParser(in_file)

The PDFDocument module is designed to interpret the structure of PDF files. Once the PDFParser object is created, the following Python code converts it into a PDFDocument object:

doc = PDFDocument(parser)

The PDFPage module contains information about a specific page within a PDF document. A given PDFPage object belongs to an associated PDFDocument object and stores data such as the page ID, contents, size, last modified time, and more.

Finally, the PDFPageInterpreter module is used to decode and process the contents of a page, while the PDFDevice module is used to translate and render those contents into the desired output format.

For more information about how to use PDFMiner, check out the project documentation, which includes multiple simple tutorials and how-to guides. For example, a common use case for PDFMiner is extracting text from a PDF file while maintaining the document’s layout, a process that is described in this tutorial.

Or you can check out the script below:

Where PDFMiner Falls Short

Although PDFMiner is a powerful and flexible tool for PDF text extraction in Python, it’s not a perfect solution. Some frequent challenges of using PDFMiner include:

Complex layouts: Highly complex document layouts, such as heavily stylized PDFs with multi-column or nested tables, can be difficult to work with in PDFMiner. In these cases, extracting text accurately requires a highly customized pipeline.
Extracting non-text elements: While PDFMiner excels at extracting text, it’s not as efficient at handling non-text components. Elements such as embedded images, graphics, annotations, or form fields can be challenging to identify and retrieve, and they may require additional tools or libraries.

In specific use cases, the output of PDFMiner may require additional post-processing—potentially including manual verification—to ensure that the results are accurate.

If PDFMiner isn’t the right choice for your PDF text extraction needs, there are various alternatives to PDFMiner that may be a better fit. These include Python tools and packages such as PyPDF2, PyMuPDF, and pdfplumber.

Combining PDFMiner with Other Libraries

PDFMiner is an excellent tool for extracting data from PDFs, but this may be just one stage in your data analysis pipeline. As a result, you may wish to combine PDFMiner with packages and libraries that have other uses, such as:

Splitting and merging PDFs: If you’re working with many PDF files, you may need to split or merge them before extracting data. Python tools such as PyPDF2 and pikepdf can help you create, read, edit, and transform PDF documents.
Structuring data: After extracting data from a table inside a PDF file, you may wish to continue storing that information in tabular format. The pandas library for data analysis in Python can save data in a two-dimensional data structure called a DataFrame, with rows and columns similar to an Excel spreadsheet.
Handling image data: In addition to text data, PDF documents may contain images that you wish to preserve. Tools such as OpenCV (a computer vision library) and Tesseract OCR (an engine for optical character recognition) can help work with scanned PDFs and images embedded in PDFs.

Real-World Use Cases of PDFMiner

PDFMiner can be used to support many types of real-world use cases when working with PDF documents. For example, it can help automate the invoice management process. Structured data (such as invoice numbers, dates, and quantities) can first be extracted from PDF invoices and then organized into a structured format with a library, such as pandas, for analysis and record-keeping.

Another viable real-world use case for PDFMiner is extracting text from PDF legal documents, such as contracts. These documents often need to be reviewed and analyzed for various purposes, such as compliance checks and legal research. Storing text from these documents in digital format can help massively speed up these analyses. Once the text has been extracted via PDFMiner, it can be used for various purposes, such as keyword searches and natural language processing (NLP) for document classification.

Finally, PDFMiner can help create searchable datasets from historical documents stored as PDFs. These documents (including newspapers, manuscripts, and archival records) are often digitized as PDFs but may lack searchable text. In combination with OCR tools, PDFMiner can help convert these documents to text, including documents with more complex layouts (such as headers, footers, and multi-column text).

Alternative To PDFMiner – How To Analyze PDFs

If you’re not comfortable with Python or perhaps you want to avoid many of the challenges you’ll face using tools likePDFMiner you can use tools like Roe AI. This way, instead of having to write a script to extract data, you can put Roe right on top of your S3 bucket and run SQL easily over your PDFs.

Meaning your team doesn’t have to wait days for you to write a script. Instead you can just use SQL.

An example of this can be seen below, where Roe’s team analyzed 40 SEC 8K from $LYFT and $UBER which totaled of 2,400 pages.

Then instead of writing a script to manually pull out all of the data fields they were able to implement an agent, as shown below to extract the key data points they were interested in.

Once the data is extracted, it can then be accessed and extracted using SQL.

From there, you can build further queries and actually ask questions about the data directly.

One of the concepts that I did like about Roe is that unlike the traditional approach of Vector search RAG where you’re often stuck “chatting” with the end data set.

Roe used SQL and LLM Vision models to keep the original data unaltered. In turn this allows them to easily tie back and show you where the data came from. I believe being able to keep this data lineage is key.

Also, as someone that has relied heavily on SQL, I foresee this approach as meeting a lot of business and reporting use cases.

Conclusion

PDFMiner is a powerful and versatile tool for extracting text and layout information from PDF files. Its strengths include detailed text extraction capabilities, support for layout preservation, and flexibility in handling various PDF formats. However, it also comes with challenges, such as difficulty processing complex layouts and limited support for non-text elements.

Despite these challenges, PDFMiner can be an excellent choice for developers looking to integrate PDF text extraction into their workflows, especially for experienced engineers. You will not just need to use PDFMiner for analysis. You’ll also need to combine it with other libraries like pandas for structured data, PyPDF2 for PDF manipulation, or OCR libraries for scanning PDFs.

We also have an article here that discusses some of the challenges you’ll face when parsing PDFs via Python.

But if you don’t have the time to process all those documents or need to complete your analysis quickly, you should contact me or Richard!

Disclosure: Seattle Data Guy does have a stake in Roe.AI

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