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Last updated on 2024年9月11日

How do you automate the data extraction process from PDFs using Python or R?

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Data wrangling is the process of transforming raw data into a more usable format for analysis, visualization, or modeling. One of the common challenges in data wrangling is extracting data from PDF files, which are often not structured or standardized. In this article, you will learn how to automate the data extraction process from PDFs using Python or R, two popular programming languages for data science.

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1 Why PDFs are tricky

PDF stands for Portable Document Format, and it is designed to preserve the layout and appearance of any document, regardless of the software or device used to view it. However, this also means that PDFs are not easily editable or searchable, and they can contain various types of content, such as text, images, tables, forms, or graphs. Extracting data from PDFs can be tedious and time-consuming, especially if you have to deal with multiple files or complex formats.

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Filippo Burnelli

Data Engineering | AI Engineering
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As any system can generate pdfs the variety of formats is huge, but we could reduce the topic to two main cases, in order to address the main issues with dealing with pdfs: "Native" pdfs and "not native" ones (scanned documents) in case of pdf is possible to easily extract the text on the other side for not native pdf is necessary a process like OCR. Text may also present in tables, to adresses such challenges Machine Learning effort are required. Pdf may also contain images and this is definitely another tricky aspects to consider

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Yugandhara Saste

Data Engineer @ Bristlecone | Google Cloud Platform(GCP) | AWS | Pyspark | Airflow | Python | SQL | Snowflake | Hadoop | Power Bi | Looker
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To automate data extraction from PDFs using Python or R, you can leverage libraries designed to read and process PDF content. In **Python**, popular libraries include **PyPDF2** for basic text extraction, **pdfplumber** for more precise text and table extraction, and **Tabula-py** for extracting tables into Pandas dataframes. In **R**, you can use the **pdftools** package to extract text and the **Tabulizer** package for pulling tabular data. Both languages require some post-processing to clean and organize the extracted data, especially when dealing with complex PDF structures.

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2 How to use Python for PDF data extraction

Python is a versatile and powerful language that offers many libraries and tools for working with PDFs. One of the most popular ones is PyPDF2, which allows you to read, write, and manipulate PDF files. To use PyPDF2, you need to install it using pip, a package manager for Python. Then, you can import it in your code and use its methods to access and extract data from PDF files. For example, you can use the PdfFileReader class to open a PDF file and get its number of pages, metadata, or text content. You can also use the extractText method to get the text from a specific page or the getFields method to get the fields from a form.

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Filippo Burnelli

Data Engineering | AI Engineering
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While the mentioned Python libraries makes it easy to access text, the Text may also be in images, like picture from natural scenes, or coming from scanned document. Detect and extract such a text in a qualitative and cost effective way, requires a capable Document AI Solutions and not only Python libraries. Certainly in some use cases such Libraries are enough

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3 How to use R for PDF data extraction

R is another popular language for data science, and it also has several packages and tools for working with PDFs. One of the most widely used ones is pdftools, which provides functions to read, write, and manipulate PDF files. To use pdftools, you need to install it using install.packages, a function for installing packages in R. Then, you can load it in your code and use its functions to access and extract data from PDF files. For example, you can use the pdf_text function to get the text from a PDF file as a vector of strings, or the pdf_data function to get the data from a PDF file as a list of data frames.

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4 How to handle different PDF formats

Depending on the type and source of the PDF file, you may encounter different formats and challenges when extracting data from it. For example, some PDF files may contain scanned images instead of text, which require optical character recognition (OCR) to convert them into readable text. Some PDF files may contain tables or graphs that are not easy to parse or extract as data frames. Some PDF files may have encryption or password protection that prevent you from accessing or modifying them. To handle these situations, you may need to use additional libraries or tools, such as pytesseract for OCR, tabula-py or camelot for table extraction, or pikepdf or qpdf for encryption removal.

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Filippo Burnelli

Data Engineering | AI Engineering
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The quality of the output of the mentioned tools and libraries, varies extremely depending from the pdf format and task that should be performed. The development in Solutions based on Transformers are very promising. Example of such Solutions could be Donut or Nougat

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5 How to automate the PDF data extraction process

If you have to deal with a large number of PDF files or a recurring task of extracting data from PDFs, you may want to automate the process using scripts or workflows. For example, you can use a for loop or a function in Python or R to iterate over a folder of PDF files and apply the same extraction logic to each file. You can also use a tool like Airflow or RStudio Connect to schedule and run your scripts or workflows on a regular basis. By automating the PDF data extraction process, you can save time and resources, and ensure consistency and accuracy of your results.

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6 How to store and use the extracted data

Once you have extracted the data from the PDF files, you may want to store it in a more convenient and accessible format, such as CSV, JSON, or SQL. You can use built-in functions or libraries in Python or R to write the extracted data to these formats, such as pandas.to_csv, json.dump, or DBI::dbWriteTable. Alternatively, you can use a tool like Excel or Google Sheets to import and export the extracted data as CSV or other formats. After storing the extracted data, you can use it for further analysis, visualization, or modeling using your preferred tools or platforms.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Matthew Jacobson

Senior Performance Analyst for the Chicago OIG
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When working on extracting information from a PDF rarely will you get everything perfectly from the script. However, if you can automate 90% of the work and manually fix the remaining portion, it's a huge efficiency gain. It's always tempting to try and get the script to handle all 100% of the work, which often isn't the best approach.

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Filippo Burnelli

Data Engineering | AI Engineering
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Extracted Text is becoming increasingly valuable with the raise of Retrieval-augmented generation (RAG). The companies can finally unlock the value in their own documents

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How do you automate the data extraction process from PDFs using Python or R?

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1 Why PDFs are tricky

2 How to use Python for PDF data extraction

3 How to use R for PDF data extraction

4 How to handle different PDF formats

5 How to automate the PDF data extraction process

6 How to store and use the extracted data

7 Here’s what else to consider

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