The easiest way to extract tables from pdf?

One of the most popular document formats to share and write data is PDF. You may come across millions of situations where you must extract table from PDFs or scanned documents. There are online tools available that provide PDF to Excel conversion but that’s not the ultimate solution. These tools may get the job done, but you still would need to manually pick data that is important to you. Not to mention how daunting this task would be but imagine the number of hours you would waste doing a task that can be easily automated. Fortunately, DocAcquire deals with such tasks seamlessly. You can extract relevant data from pdf without wasting your precious time. 

What is Table Data Extraction? 

Tables are one of the primary structuring elements in a document like PDFs. To reuse the table data appropriate extraction methods are needed. Table data extraction from PDF’s is the process of retrieving structured data arranged in a tabular format from PDF documents. Many PDF files contain important data stored in tables, such as reports, invoices, and spreadsheets, which are difficult to extract due to the non-editable nature of PDFs. The method to extract table from PDFs helps convert this data into usable formats like CSV, Excel, or databases for easy analysis. 

In today’s digital landscape, efficient table data extraction from PDFs is essential for businesses seeking to unlock valuable insights and streamline workflows. By using extract table from PDF methods and tools, organizations can unlock valuable data, enhance productivity, and make more data-driven decisions. This blog explores the importance of extracting table data from PDFs, common challenges faced, different methods for extraction, and a detailed guide on using DocAcquire platform for seamless data extraction. 

Importance of Extracting Table Data from PDFs 

Before diving into how to extract table from PDFs, let’s look at why this process is so essential for organizations and professionals: 

• Data Accessibility: Accessing structured data from PDFs facilitates analysis, reporting, and integration with other systems. 

• Data Migration: Moving data from PDFs to databases or other applications can be crucial for maintaining up-to-date information. 

• Automation and Efficiency: Manually copying tabular data from PDFs can be time-consuming and prone to errors. Automated extraction helps streamline workflows, allowing data to be processed more efficiently and helps ensure accuracy, especially for organizations handling high volumes of data. 

• Informed Decision-Making: Having access to structured tabular data makes it easier to draw meaningful insights. Accurate data extraction from PDFs allows decision makers to evaluate information more effectively and take informed actions. 

• Preserving Data Accuracy: When data is trapped in a PDF, there’s a risk of loss in accuracy due to manual re-entry errors. Automated extraction tools ensure that the data is transferred accurately from the document, preserving its integrity for future analysis. 

• Accessibility and Collaboration: Once data is extracted from a PDF, it becomes easier to share and collaborate across departments or teams. Tabular Data in an editable format can be updated, reviewed, or merged with other datasets, facilitating collaboration and teamwork. 

Use cases of PDF documents  

Pdf’s have become the most widely used mode of communication between systems, individuals, and companies. It is considered a standard version of documents. Some of the popular use cases of PDF documents in the business world are: 

• Invoices 

• Purchase Orders 

• Shipping Notes 

• Reports 

• Presentations 

• Price & Product Lists 

• HR Forms  

Challenges in Extracting Table Data from PDFs 

Extracting table data from PDFs can be challenging due to several factors. Here’s a look at the main challenges: 

• Lack of Structured Data: Not all PDF’s follow a consistent format. Tables might vary in design, spacing, alignment, or even be split across multiple pages, making automated extraction more error prone.  

• Varied Table Formats: Tables within PDFs can have diverse layouts and formats, complicating extraction. 

• Non-Text Elements: Data may be presented in graphic formats (e.g., charts or scanned tables, making it impossible for standard extraction tools to read the content without Optical Character Recognition (OCR). 

• Resolution Issues: Low- resolution scans can lead to blurry text, making it difficult for extraction software to accurately read the content. 

What are the possible solutions?  

The huge volume of PDFs exchanged daily means that there is a need to find a solution that can extract tables from PDF automatically and quickly. Also, spending time to extract and export the data from pdf into any other third-party system will be a costly affair. The primary issue that leads to other bigger problems in data extraction from pdf is that PDFs were designed only as a data output format and not as a data input format. It helps in printing the data formatting exactly as you see it on your screen. However, as far as a table format is concerned, a PDF does not have any internal structure for it. And that’s the reason it is a cumbersome task to extract tables from PDF. You can find many online tools that claim to extract data from tables. However, most of these end up truncating the data or distorting the internal table structure. Hence, the data you extract becomes practically useless, as you manually must input it into any other third-party system. 

One of the most common solutions for extracting data from PDF is to use an OCR. However, before you go and start the data extraction process there are various things that need to be considered. Firstly, whether you are processing an image-based document or text file.  Most of the OCRs work fine with text-based images, however, they struggle with processing image-based documents. Some of the OCRs that can be helpful are Amazon Textract, Google Vision, etc. OCR or optical character recognition technology, which helps to recognize the data inside a file and converts it into a searchable and editable form. 

But is that enough? For a business that requires processing thousands of documents in a month, a simple OCR will sort the problem of data processing. Well, yes! If you are ready to handle the tons of data after extraction, then a simple OCR will do. But you can save hours of hard work and effort by leveling up and switching to automated solutions provided by DocAcquire. DocAcquire helps to Extract table data from a variety of document types like; Invoices, Purchase Orders, Proof of Delivery, Contracts, Insurance Claims, Bill of Lading, and many more. It provides a flexible integration engine to extract and cleanse tabular data by using out-of-the-box powerful and intuitive tools. We have been heavily investing to deliver an engaging and simple user experience that enables our users to automate invoice workflows and seamlessly export the data to other systems. 

Ways to Extract Tables from PDFs (with Pros and Cons) 

Several methods can be employed to effectively extract tables from PDFs, ranging from manual-copy pasting to using sophisticated software tools. Here are some of the most effective approaches: 

1. Manual Extraction: 

It refers to the process of retrieving and organizing data that appears in table format within a PDF document without relying on automated tools. This involves copying or retyping the data into another application, such as a spreadsheet, to recreate the table format. 

Pros:  

• Full control over data extraction. 

• Simple and Straightforward for small datasets. 

Cons:  

• Time-consuming and error-prone for large volumes of documents. 

2. Tabula: 

Tabula is a free, open-source tool crafted by journalists for journalists, specially built in order to extract tables from PDF files. Users can easily navigate its intuitive interface to select and extract table data, with the option to convert to CSV or Excel formats. 

Pros: 

• Tabula offers a straightforward UI, ensuring seamless navigation and operation. 

• It excels in efficiently extracting data from small, uncomplicated tables, enhancing productivity. 

Cons: 

• Tabula may lack features for handling intricate table structures, limiting its suitability for complex data extraction tasks. 

• Selecting tables manually can be tedious, especially with extensive documents. 

• Longer tables may compromise accuracy, leading to inaccuracies in extracted data. 

• Performance relies on the quality of the source PDF, impacting extraction reliability. 

3. Excalibur: 

Excalibur simplifies PDF table extraction using the Camelot library. With customizable parameters, users can define table regions for optimal extraction. It supports exporting tables to CSV and Excel, catering to diverse needs. Excalibur makes it easy to extract tables from PDFs and convert PDF tables to Excel. 

Pros: 

• Custom extraction parameters enable precise handling of various PDF layouts. 

• Output formats include CSV and Excel, facilitating seamless integration into data workflows. 

• Highly configurable settings empower users to fine-tune extraction processes according to specific requirements. 

Cons: 

• Limited to processing PDF documents. 

• Deployment can be challenging, and certain modules offer limited customization options. 

• Complex multi-page tables may pose challenges for extraction. 

4. pdfplumber: 

pdfplumber is a Python library designed for extracting tables from PDFs. Leveraging pdfminer as its foundation, pdfplumber offers a robust PDF parsing engine capable of handling diverse PDF layouts and formats. 

Pros: 

• Enables seamless access and manipulation of extracted text and table data using Python, facilitating adaptable data processing tasks. 

• Provides easy access to comprehensive details about each PDF object. 

• Offers higher-level and customizable methods for extracting text and tables. 

Cons: 

• Primarily suited for machine-generated PDFs rather than scanned documents. 

• Does not support Optical Character Recognition (OCR) functionality. 

• Text extraction quality may be inferior especially for complex or poorly formatted PDFs. 

5. PDFTables: 

PDFTables is an online tool, specializes in PDF table extraction, enabling users to extract tables from PDFs and convert them into Excel, CSV, or XML formats. It quickly finds tables within PDF documents and converts PDF table to Excel and offers various formats for the converted data. 

  Pros: 

• PDFTables excels in swiftly converting substantial volumes of information. 

• It offers users the flexibility to choose from multiple output formats, including Excel, CSV, and XML. 

• Facilitating bulk PDF file conversion, PDFTables also provides an API for automating table extraction tasks. 

• Users can avail themselves of PDFTables’ services for free for up to 25 pages. 

Cons: 

• PDFTables may lack advanced customization options compared to other tools, potentially restricting control over the extraction process. 

• Users may find the tool lacking in features for customizing output formats. 

• PDFTables does not offer integration with cloud services. 

• Does not support Optical Character Recognition (OCR) functionality. 

How to Extract Tables from PDFs Using DocAcquire 

DocAcquire is a robust software solution designed for efficient table data extraction from PDFs. It can extract tabular data from PDFs and can help you to automate your data extraction without any hassle. Here’s how you can leverage Doc Acquire: 

Introduction to DocAcquire: 

DocAcquire is a powerful, cloud-based platform that leverages artificial intelligence to automate the process of data extraction from a wide range of documents. It is designed to transform manual data entry tasks into automated workflows, thereby saving time, reducing errors, and enhancing customer experience. 

The platform can extract data from any document type, including invoices, and integrates seamlessly with various business systems such as ERP, CRM, and Finance. It uses machine learning to recognize and extract key data from complex documents, ensuring accurate and efficient data capture. 

In addition to data extraction, DocAcquire offers verification and enrichment features to ensure perfect data flows into your business systems and makes it easy for exceptions to be handled even faster. 

Used across various sectors including Accounts Payable, Logistics, and Insurance, DocAcquire is trusted by many organizations for its ability to reduce manual effort, processing time, and cost. It is a reliable solution for businesses looking to streamline their data extraction processes and improve operational efficiency. 

Key Features 

• Extracts both structured and unstructured data from a variety of table layouts. 

• Integrates with existing business systems for end-to-end processing. 

• Uses highly accurate OCR for high quality text extraction from scanned PDFs. 

• Quick and straightforward setup and configuration. 

• Specializes in processing intricate documents like insurance contracts, claims, and KYC forms. 

• Send extracted data to any business application on premises or on the cloud using the standard REST API endpoint. 

• You can even pull out the extracted data using REST API endpoints in JSON format. 

• Offers a user-friendly interface for verifying and correcting the extracted data. 

• Reduces manual effort and processing time, resulting in cost savings. 

• Consolidates and processes documents from various sources. 

• Accurately classifies, recognizes, and extracts key information from documents. 

• Validates, enriches, and reviews data to ensure accuracy. 

Steps to Extract Table Data Using DocAcquire: 

1. Create a Document Type 

Go to the Documents Tab and click on the “Plus Icon” to add a document type. 

2. Create line items for the Document Type 

Go to the Tables tab in the side navbar and define rows for them.  Click here to know more. You can specify the data type of each of the line items you create.

3. Train the document for table extraction. 

4. Uploading and processing PDF documents for data extraction. 

 Select the desired document type. This ensures accurate processing and data extraction based on the specific document template. 

5. Verifying, manually extracting, and exporting extracted data for further analysis: 

DocAcquire provides flexibility to review, and export extracted data. In addition to automated extraction, users can also manually extract tables by clicking the Extract Table button, enabling precise control over data capture when automated recognition might need adjustments. 

6. View the excel sheet. 

Check out a quick demo of DocAcquire to see these steps in action.

FAQ (Frequently Asked Questions) 

1. How accurate is table data extraction from PDFs using DocAcquire? 

DocAcquire leverages advanced AI algorithms to achieve high accuracy in extracting table data from PDFs. However, accuracy may vary based on the complexity of the document layout. 

2. Is DocAcquire suitable for extracting tables from scanned PDFs? 

Yes, DocAcquire can extract tabular data from scanned PDFs using OCR (Optical Character Recognition) technology. It can detect text from images and convert it into machine-readable formats, allowing you to capture data from tables even in non-digital (scanned) PDFs. 

3. How does DocAcquire handle multi-page tables? 

DocAcquire can extract tables that span across multiple pages by using an intelligent table append feature. It ensures that multi-page tables are extracted as one coherent table, saving time and maintaining accuracy during the data extraction process. 

4. Do I need programming skills to use DocAcquire? 

No, DocAcquire is designed to be user-friendly and does not require programming skills. However, for advanced customization or integration with other systems, some technical knowledge may be helpful. 

5. How long does it take to set up DocAcquire for extracting tables from PDFs? 

The setup time for DocAcquire depends on the complexity of your extraction requirements. Our team can assist you in configuring the software to meet your specific needs efficiently. 

Conclusion 

In conclusion, extracting tables from PDFs is a critical process for accessing and utilizing valuable data within documents.  Despite the challenges posed by PDF formats, innovative solutions like DocAcquire empower businesses to automate and streamline data extraction workflows efficiently.  

We hope this article helped you get started with getting table data from invoices and purchase orders. If you want a helping hand with the setup reach out to us.   

Want to try DocAcquire? Just let us know.