Extract text from pdf – Automate & free up your time

What is PDF?

PDF (Portable Document Format) is a file format that is used to present and exchange documents reliably, independent of software, hardware, or operating system. PDF was invented by Adobe and is now an open standard maintained by ISO. PDFs are widely used in various business applications due to their versatility, reliability, and ease of sharing across different devices. PDFs ensure that documents maintain their formatting, fonts and layout when shared across different devices and operating systems, making them ideal for professional communication. This is vital for contracts, reports, and official documents where precision matters. Extracting data from PDF files is crucial for businesses because it enables the efficient and automated retrieval of valuable information from documents, reducing the manual effort and enhancing accuracy.  

Why is data extraction from PDF files important:  

In today’s digital age, businesses handle a significant volume of PDF documents daily. Extracting text and data from PDFs is often a manual, time-consuming task which demands a huge workforce. As a result, it slows down the business, hence adds more costs and introduces manual errors. By automated data extraction, businesses can save time compared to manual data entry or searching, leading to faster processing of documents. When data is extracted and processed correctly, it becomes easier to analyze leading to more informed decision making. PDF files often contain structured data, such as tables, invoices, contracts, and other financial documents. This is crucial in industries like finance, legal, healthcare, and logistics, where extracting key data elements from pdf documents (e.g., Invoice Number, Date, Total, etc. from an Invoice) is routine. 

This blog delves into the challenges of trying to extract text from PDF files, explores various methods to extract data from PDF documents, and highlights how DocAcquire can streamline the process of extracting text from PDFs, saving your both time and resources. 

Specific use cases to extract text from PDF files 

1. Supplier Invoices. 
2. Purchase Orders. 
3. Insurance Contracts & Claims. 
4. Customer onboarding forms. 
5. Standardized Reports. 
6. Electronic Health Records. 
7. Shipping documents. 
8. Proof of delivery. 
9. and the list goes on and on… 

Challenges in Extracting data from PDFs 

 Extracting data from PDFs poses several challenges, especially when trying to automate the process or handle complex documents.  

• Non-Editable Content:  Many PDFs contain scanned documents that are essentially images, requiring Optical Character Recognition (OCR) to convert images into text for data extraction. Extracting data from image-based PDFs, typically produced by scanners, can be extremely time-consuming and error-prone without advanced OCR technology. While OCR-based document scanners are now available to convert these image-based files into searchable PDFs, manual text entry is still common in cases without such technology, adding significant labor. 

• Complex layouts: Many PDFs contain complex layouts, such as tables, multi-column text, or embedded images, which makes it challenging to extract relevant data accurately using automated tools. 

• Time consuming: Manual data extraction is labor-intensive and can take a significant amount of time, especially for large documents or when dealing with a high volume of files. Extracting data from image-based PDFs without OCR technology exacerbates this issue. 

• Error-Prone:  Manual data entry introduces errors, especially with complex documents. Misreading data, transcription mistakes, or overlooking important information can lead to inaccuracies in the extracted data. 

• Manual Processing:  Processing a high volume of PDFs manually for data entry requires significant workforce, slowing down business operations and increasing costs. 

• Document Variety:  Different types of documents, such as invoices, purchase orders, and contracts, often require customized extraction approaches, further complicating the process. 

These challenges highlight the need for efficient methods and tools, such as OCR technology and automation solutions, to streamline the data extraction process from PDFs, reducing manual effort and minimizing errors. 

Should I automate extracting text from pdf files? 

That depends on the volume, type (image/searchable), and the amount of text/data you need to process from each pdf file; 

 Single/Multipage low volume: 

 To be honest, if we are talking about a few pdf files per day, it’s not a huge challenge to manually extract data and key in that data in your   line-of-business system. So, it won’t make any sense to introduce automation, as it is going to be overkill. 

Single/Multipage high volume: 

In this case, the data entry operator has to individually open each pdf file, locate the data fields from the correct pages, then copy/paste data in case of searchable pdf. It would get harder for the operator to manually type in the text in the destination system when the pdf is not searchable. Formatting the dates and numbers during the data entry process would further make it more time-consuming and error prone. 

So, using modern data capture cloud-based software like DocAcquire to automate the data entry process would yield a huge ROI to any business. 

Methods to Extract Information from PDF Files 

If you’re looking to extract data from PDF files, there are several tools and methods available, depending on your specific needs: 

• Full Page Data Extraction: If you want to convert an entire PDF into another format, you can use tools like: 

1. 1. Adobe Acrobat 
   2. PDF To Text
   3. Online OCR – Allows you to convert PDF to Word, Excel, or Text. 
   4. There are many other tools available—just search for “convert scanned PDF to text” for more options. 

However, if you don’t want to extract all the data and only need specific key data fields (e.g., Invoice Date, Invoice Number, Tax, Total from a Supplier Invoice), and if you need to store the extracted data in structured formats like Excel, SQL Server, SharePoint, or your business system, then you require automated data capture software. This software uses Optical Character Recognition (OCR) and Machine Learning to extract specific data fields efficiently. 

Here are different methods of extracting information from PDFs: 

1. Manual Extraction: Manually extracting text from PDFs involves reading the document and typing the data into a system. This method is feasible for low-volume and straightforward documents but becomes impractical as volume and complexity increases. This approach is labor-intensive and often leads to higher error rates due to human fatigue and oversight.
2. Copy-Paste from Searchable PDFs: For PDFs that are searchable, data entry operators can copy and paste text into business applications. This method reduces the effort compared to manual extraction but still carries a risk of errors and is not suitable for image-based PDFs. Searchable PDFs allow users to highlight and copy text directly, making the process faster but still subject to inaccuracies if data is not reviewed carefully.
3. Optical Character Recognition (OCR): OCR technology is used to convert scanned documents into searchable text. Modern OCR tools can handle a variety of fonts and layouts, improving accuracy and efficiency. However, OCR’s effectiveness can be limited by the quality of the scanned documents. Poor resolution, complex formatting, and handwritten text can pose significant challenges to OCR accuracy, necessitating additional verification and correction steps.
4. Automated Data Capture Software: Automated data capture software like DocAcquire leverages OCR and machine learning to efficiently extract specific data fields from PDFs and scanned documents, reducing manual effort and increasing accuracy. The majority of modern automated data capture platforms are built on a workflow system.

          A typical document extraction workflow goes through the following stages;

1. 1. To extract only specific data fields, you can train the software with a bunch of documents.
   2. Configure the workflow to import, classify, extract, verify, and export the extracted data to your chosen destination.

          Workflow steps of an automated data capture software:

• • Import:
    A variety of file types like pdf files and scanned images can be uploaded to data capture software. These documents can come from an array of sources; 
    • Email Inboxes 
    • Network Folders 
    • Mobile Phones (Document Scanner Apps) 
    • Directly uploaded/imported into the software 
    • Cloud-based file storage applications like Google Drive , Box , Dropbox, etc. 
    • DocAcquire has a strong integrations engine that enables the import of documents from a wide variety of sources.
  • Classify:
    Every document is classified based on its layout and content. Once classified, the document is ready for the next stage of data extraction. The system marks the document as Unclassified if it can’t recognize the document layout and content. In such a case, the document needs to be trained.
  • Extract:
    Once the document is classified, based on the initial training the required data/fields are extracted. Once the data extraction is done, the document is sent for the next stage of the workflow for verification. 

• • Verify:
    This is the stage where a human operator comes in and verifies the extracted data, fixes any potential errors, and marks the document Ready for the next stage for Export. This is normally a less time-consuming process as the majority of the heavy lifting is already done by the data extraction software. The involvement of a human to verify the extracted data guarantees the maximum accuracy of data after data extraction. 

• • Export:
    At this stage, a document extraction is already done, and data is verified by a human operator, the data is then exported to the selected destination. 

How DocAcquire Can Automate PDF Document Data Extraction Workflows 

Overview of DocAcquire 

DocAcquire is a modern cloud-based data capture software that automates the extraction of data from a variety of file formats such as PDFs, PNG, JPEG, and TIFF. It uses advanced OCR and machine learning algorithms to handle both searchable and image-based PDFs. By default, DocAcquire uses the OCR engine called AWS Textract to read the text from documents. By automating the extraction process, DocAcquire helps businesses save time, reduce errors, and increase operational efficiency. 

Capabilities of DocAcquire 

With DocAcquire, you can extract specific data points from PDFs, such as invoice numbers, dates, totals, and more. The software can handle multi-page documents and extract data from tables, making it versatile for various business needs. Additionally, it supports various document types including invoices, purchase orders, insurance claims, and more, ensuring comprehensive data capture across different business functions.

Ideal Use Cases for DocAcquire 

DocAcquire is perfect for businesses looking to streamline their document processing workflows. It is especially useful for high-volume document processing, reducing the need for manual data entry and minimizing errors. The software is ideal for industries such as finance, healthcare, logistics, and any other sector that deals with large amounts of paperwork. It ensures data consistency, improves processing speed, and enhances overall productivity. 

Common Applications of DocAcquire 

1. Supplier Invoices: Automatically extract invoice details like dates, numbers, and totals. This reduces the time spent on manual data entry and ensures accurate record-keeping.
2. Purchase Orders: Extract key data fields to streamline order processing. Automating this process ensures that purchase orders are processed quickly and accurately, improving supply chain efficiency.
3. Insurance Contracts & Claims: Process claims efficiently by extracting relevant data. This helps insurance companies manage claims more effectively, reducing processing times and improving customer satisfaction.
4. Customer Onboarding Forms: Speed up onboarding by automating data entry from forms. Automating this process ensures that new customer information is captured accurately and quickly, enhancing the customer onboarding experience.
5. Standardized Reports: Extract data for analysis and reporting. Automated data extraction from standardized reports helps businesses generate insights faster, supporting better decision-making.
6. Electronic Health Records: Extract patient data accurately from health records. This is crucial for maintaining accurate and up-to-date patient records in healthcare settings, improving patient care and compliance.
7. Shipping Documents: Automate the extraction of shipping details for logistics. This ensures that shipping information is processed accurately, improving tracking and delivery efficiency.

Key Features of DocAcquire for PDF Data Extraction 

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

• Can handle huge documents with hundreds of pages, even those with high complexity and poor layouts. 

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

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

• 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. 

• You can also extract your data directly to Microsoft SQL Server. 

By leveraging DocAcquire’s advanced capabilities, businesses can automate the extraction of data from PDFs, freeing up valuable time and resources while enhancing accuracy and efficiency.

DocAcquire’s – Cognitive Invoice is a platform built on deep learning which makes invoice data extraction a breeze. Here is a quick sneak peek of the platform, you can see how easier is it to get up and running – there’s no need to build and maintain templates.

FAQs 

1. What types of PDFs can DocAcquire process? 

DocAcquire can process both searchable and image-based PDFs. It uses advanced OCR and then applies highly intelligent machine learning techniques to convert scanned images into useful data, making it suitable for a wide range of document types. 

2. How accurate is DocAcquire’s data extraction? 

DocAcquire uses advanced OCR and machine learning algorithms, ensuring high accuracy in data extraction. The system also includes a user verification step to correct any potential errors. 

3. Can DocAcquire handle multi-page documents? 

Yes, DocAcquire supports multi-page documents with hundreds of pages and can seamlessly extract data from all of them. 

4. Is it possible to extract data from tables within PDFs? 

Yes, DocAcquire can extract structured data from tables within PDFs, making it ideal for documents such as invoices and other custom document types. 

5. How does DocAcquire handle poor-quality scans?

DocAcquire can be configured to use different OCR engines, such as Google Vision and Amazon Textract. Both OCR engines are highly capable and utilize machine learning to improve text extraction accuracy. These OCR engines can be swapped to achieve the best accuracy for each use case. 

I hope you found this blog helpful and if you have any specific questions, please Contact Us and we would be more than happy to answer any of your questions.