Alternatives to Azure Document Intelligence Studio: Exploring Powerful Document Analysis Tools

Document Intelligence Studio is a data extraction tool that can pull unstructured data from diverse documents, including invoices, contracts, bank statements, pay stubs, and health insurance cards. The cloud-based tool from Microsoft Azure comes with several prebuilt models designed to extract data from popular document types. However, you can also use labeled datasets to train custom models.

Many organizations adopt Document Intelligence Studio to automate documentation and data-retrieval processes. For example, a bank might use Document Intelligence Studio to extract relevant unstructured data from mortgage applications and add the information to a customer relationship management (CRM) platform.

While this might sound similar to scanning technologies like optical character recognition (OCR), Document Intelligence Studio uses machine learning and artificial intelligence to understand unstructured data on a deeper level. Document Intelligence Studio also doesn’t require as much manual oversight outside initial training for custom documents. Of course they’ll try to convince you it can be 100% automated but I’d make sure to implement some data quality checks.

While Azure Document Intelligence Studio can make sense for some businesses, it isn’t the perfect solution for every organization. For instance, you might find that Document Intelligence costs more than you want to pay, especially if you don’t already use other Microsoft services.

Below, you’ll learn more about alternatives to Document Intelligence Studio to make an informed decision before investing in a tool for parsing PDFs and other document types.

Key Features of Document Intelligence Studio

Azure Document Intelligence Studio might suit your needs well if you want key features like:

• Integration with other Microsoft services, including AI tools
• Workflow automation
• Enhanced document processing that can learn where to find specific information on a form
• Custom modeling that learns to extract data from specific fields within documents
• AI-driven analytics for unstructured data
• Metadata for key-value pairs and other data relationships
• Support for table labeling
• Custom containers for deploying AI

Factors to Consider When Choosing an Alternative

It’s always a good idea to evaluate alternatives to Document Intelligence Studio before committing to any platform or tool. More likely than not, you will need to weigh several factors to determine which alternative is right for you. An ideal solution might not exist, but you can still select a platform, tool, or library that meets most of your needs.

Technical Requirements

Technical requirements will differ depending on your existing tech stack and preferences. For many users, it’s critical to choose alternatives to Document Intelligence Studio that integrate with other platforms. If your organization relies heavily on AWS services, it might make sense to choose AWS Textract. If it uses Google services extensively, Document AI might fit better. Before choosing any PDF parsing tool, check its documentation to make sure it can communicate with the tools you already use.

You might also need to consider whether you want a cloud-native or on-premises solution. If you want scalability but don’t want to invest in on-site servers, it probably makes sense to choose a cloud-native option like Roe AI or Document AI. If you prefer an on-premise solution, review your options to find one that can work on location without relying on cloud servers.

Some companies offer cloud-native and on-premises options.

Use Case Alignment

Some companies make alternatives to Azure Document Intelligence Studio to meet the needs of specific industries. For example, Roe AI is specifically focused on helping make accessing unstructured data via SQL easy.

Other developers take a broader approach that lets you customize processes for your unique needs. This might take more effort, but it could also mean you get a fine-tuned model for your documents.

Importantly, you should look for solutions that accommodate your needs and regulations. If you need to pull data from healthcare documents, choose a solution that can conform to HIPAA. If you work with financial documents, you might need software that can follow SOX and PCI DSS regulations.

Ease of Implementation

Who will use the solutions once you adopt them? If semi-technical employees will use them, look for tools with user-friendly graphical interfaces and prebuilt models. Otherwise, you will likely spend a lot of time training them how to perform basic tasks.

You can draw from a larger pool of options when your employees have technical skills. Even with skilled personnel, though, you need to ensure ease of implementation. For example, PDFMiner will only work well for people with Python skills. Plenty of tech workers don’t know Python, so you need to ensure you choose a tool that matches the employee’s skill set.

Great Alternatives to Document Intelligence Studio

If you want to explore some alternatives to Document Intelligence Studio, start with these top options:

Google Cloud Document AI

Google Cloud Document AI leverages machine learning to extract both structured and unstructured data from various types of documents. The platform supports pre-trained and custom models, allowing users to adapt the tool to their specific needs and seamlessly integrate it with storage and analytics tools. Its user-friendly approach and accuracy make it a strong competitor in document processing.

If you’re looking for a guide to get started, then you can use this one.

• Utilizes machine learning to process structured and unstructured data from documents.
• Offers pre-trained models for common document types and allows custom model creation for specific use cases.
• Seamlessly integrates with other Google Cloud services, such as BigQuery and Cloud Storage.

Tesseract OCR (Open Source)

Tesseract OCR is a robust open-source tool for extracting textual data from image files. It supports a wide range of file formats, including PNG, JPEG, TIFF, and more. It was developed initially by HP and later released as an open-source project, it is now maintained by Google. Tesseract supports a wide range of languages and scripts and is widely used for tasks like document digitization, data extraction, and text recognition in scanned documents, photographs, or screenshots.

• Supports various image formats, including PNG, JPEG, and TIFF.
• Includes multi-language support with pre-trained data for over 100 languages.
• Allows custom language or font training for niche applications.
• Can be paired with image preprocessing tools like OpenCV to enhance accuracy.

PDFMiner And PyPDF2

There are multiple Python libraries that can help you parse PDFs. For example PyPDF2 and PDFMiner. Both of these libraries are specifically designed for parsing and extracting data from PDFs. It focuses on unstructured data extraction and provides features like extracting tables of contents. Of course, writing custom solutions has it’s own set of challenges when working with PDFs.

• Both are Python libraries specifically designed for parsing and extracting text from PDFs.
• PDFMiner focuses on extracting unstructured data, including layout and text metadata.
• PyPDF2 excels in tasks like splitting, merging, or decrypting PDFs but is limited in extracting structured content.
• Useful for custom solutions where developers need control over PDF data extraction.

Roe AI

If you’re not comfortable with Python or you just want to be able to run large queries over your PDFs, you can use tools like Roe AI. Roe has built-in data connectors to unstructured data sources. This includes data sources such as S3 which allow you to query data directly from PDFs via SQL and their agents.

So instead of needing to manually write Python to parse PDFs, then load that data into a database, then write a query. You can just write a query!

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. 

• Offers SQL-based querying over unstructured data, including PDFs, without extensive preprocessing.
• Provides data connectors for popular sources like Amazon S3, enabling seamless data integration.
• Features agent-based extraction for scalable and automated processing of large document sets.
• Leverages machine learning to extract text, forms, and tables from documents.

AWS Textract

AWS Textract is a machine learning-powered tool designed to automatically extract text and data from documents. It can process a variety of unstructured and semi-structured documents, including those containing handwritten content. By accessing AWS’s scalable infrastructure, Textract is particularly useful for organizations already using the AWS ecosystem.

• Leverages machine learning to extract text, forms, and tables from documents.
• Handles both printed text and handwritten content, making it versatile for various document types.
• Fully integrated into the AWS ecosystem, benefiting organizations already using AWS services.
• Offers scalable infrastructure for high-volume document processing needs.

OpenAI GPT Models with Custom Prompts

It goes without saying that ChatGPT can also be a reliable option here. After all, it doesn’t just offer a UI for you to ask questions to. The underlying technology has diverse use cases that respond to custom prompts. When accessed via API, GPT-4’s PDF parsing capabilities make it a useful option for companies that need to automate data extraction. Now it will take a little more effort than some of the solutions referenced above that are specifically created to use a combination of LLMs and document parsing but it also can’t be forgotten.

How to Evaluate Alternatives

After comparing the popular alternatives to Azure Document Intelligence Studio, you will probably have a short list of options to evaluate further. Take the following steps to find the tool that best fits your needs.

Pilot Testing

Most companies will let you try their products before you commit. During your trial, use pilot testing to determine how each solution functions within your IT ecosystem.

Pilot tests are small-scale trials that let you assess accuracy and efficiency. You might want to create a list of metrics to make comparison easier. At the very least, you should keep track of the output each creates for the types of documents you plan to process.

Feel free to push each tool to the point of failure. Your needs could grow considerably in the future, so you want to know what each solution performs in fringe scenarios.

Performance Metrics

The key performance indicators you track will depend on your use case and expectations. Still, there are some performance metrics that everyone should pay close attention to. Make sure your list includes:

• Accuracy: How many mistakes did the tool make when given typical — and some of your most challenging — documents to parse? Keep a running tally and percentage for easy comparison.
• Speed: How long does it take each tool to extract the required data from, say, 1,000 documents?
• Scalability: Was the solution able to scale up and down to meet each project’s evolving needs?

Not all of these metrics will always carry the same weight. Maybe one tool can pull data from 1,000 documents within 10 minutes, while another tool takes 15 minutes to do the same job. If the fastest option has a lower accuracy rate, you might decide to adopt the slower one. Otherwise, your staff might end up spending a lot of time correcting inaccurate data. In the long run, the higher processing speed might not always mean a tool truly works faster.

Support and  Documentation

Every technical tool should come with documentation that tells you how to use its features. Look for solutions with well-organized documentation that helps you learn the basics quickly. It should also include instructions you can follow to fulfill tasks within your specific use case. The level of documentation you expect, however, might depend on your level of technical experience.

At some point, you will almost certainly need support from the tool’s developer. Early interactions with a company don’t always tell you whether it communicates well with clients. Read several reviews online to learn about the experiences other users have had with client support services.

Future Trends in Document Analysis

Future trends in document analysis should make parsing PDFs and other documents easier than ever. Have the alternatives to Document Intelligence Studio positioned themselves to embrace these trends?

Ask the companies to give you more information about:

• How they plan to integrate more sophisticated AI models into their processes.
• Whether they have — or will soon have — a low-code/no-code platform that makes document processing easy for non-technical users.
• If the tools can integrate multimodal extract models to collect text, images, and table data.
• Whether the solutions take advantage of real-time edge computing solutions.

Knowing the alternatives to Document Intelligence Studio should help you find a solution that aligns with your needs. Take advantage of every benefit as you compare your options.

Sign up for demos, trials, and other opportunities to learn more about how each solution works.

If your team needs help parsing PDFs or unstructured data, feel free to set-up some time with our team!

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

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