OCR vs Machine Learning for Real Estate Data Extraction

Published by
Nico Lassaux
January 23, 2023
OCR vs Machine Learning for Real Estate Data Extraction

What is OCR?

Optical Character Recognition (OCR) involves converting documents like contracts and property records into editable, searchable digital formats. It can be used in real estate to streamline document management, reduce manual data entry, and enhance compliance and record-keeping. By integrating OCR with property management systems and real estate analysis workflows, investors can improve efficiency and customer service, offering quick access to important information.

Should my Real Estate Business Use OCR or ML to Parse Documents (or both)?

These days, savvy real estate companies are looking to automate their transaction pipelines, and technologies like Optical Character Recognition (OCR) and Machine Learning (ML) are important in this process. In real estate, businesses need to extract data from documents such as deeds, offering memos, schedules of real estate owned and purchase agreements, and they need to know the extracted data is accurate.

When real estate professionals look at all the options for automated data extraction, there are so many products out there that it can be confusing trying to find the best one. As artificial intelligence technology advances, the lines between OCR and ML are becoming blurred. OCR is an important component of machine learning, particularly for document extraction, and machine learning is a critical part of processing and improving OCR outputs.

What makes the most sense for your real estate business? If you are trying to make a decision today, there are a few key differences between these technologies to think about.

What is Optical Character Recognition?

Optical Character Recognition (OCR) is a technology that transforms scanned images of text into machine-encoded format. OCR algorithms analyze images to locate and identify each individual character in a string of text. The resulting digital text can then be edited or searched. OCR has actually been in use for decades, but has become increasingly prevalent with the rise of digitization and data extraction technologies.

Today, OCR algorithms are increasingly utilizing machine learning techniques, particularly neural networks, to improve the accuracy of text recognition. A neural network can process each letter of a text line, taking into account the context of the preceding and following characters, to predict the character, even if it is partially obscured (for example by poor scan quality). This provides a clear advantage over simply extracting the text.

What is Machine Learning?

Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to learn from data without being explicitly programmed. It is used in various industries and domains, but particularly in the fields of computer vision and natural language processing. The books "Artificial Intelligence: Foundations of Computational Agents" by David Poole and Alan Mackworth, and "Deep Learning" by Yoshua Bengio, Ian Goodfellow, Aaron Courville, describe how machine learning algorithms are used to automatically interpret complex representations of data and make predictions or decisions based on that data without human intervention.

Machine learning doesn’t exclude OCR though. When ML is applied in data extraction processes, it often includes the use of OCR to improve text extraction and recognition. One of the key advantages of ML is its rules-based approach, which allows for capabilities beyond simple data extraction. With ML, the OCR outputs can be fine-tuned to specific problems or documents, enabling the identification and classification of key information found in the text.

For example, while OCR technology can extract the letters "Sam Zell" from a property deed (and Sam Zell owns quite a few properties), ML technology can help identify that these letters make up a person's name. In a specific document like a deed, it can also identify that they are the Grantor listed on the document because it looks at the data and context surrounding the extracted entity. Plus, the more documents processed, the better the technology will become as the AI continues to learn.

Which is Better for Data Extraction?

It really depends on what you need. For extracting data from documents, the choice between Optical Character Recognition (OCR) and Machine Learning (ML) depends on the complexity of the documents in question.

Optical Character Recognition is perfect if you are extracting data from only one type of document - the simpler the document, the better. OCR is particularly effective for extracting data from templated documents such as invoices and receipts that have little variation in structure.

OCR technology may struggle with more complex documents though, because it relies on patterns that are easily broken when trying to extract data from some more complex forms and other files. Format changes often pose a challenge for OCR.

In contrast, ML excels at extracting data from any document type, including those that are highly varied and complex. Machine learning data extraction works on any document, and ML especially shines with complex documents that have a great deal of variation.

The two are not completely separate though. ML often incorporates OCR as part of its process. What machine learning allows OCR to do is delegate tasks to models, avoiding complex math and rules. The models are able to learn these tasks incredibly well once you have the data.

While ML is extremely powerful, it does require some categorization and labeling work to establish a strong foundation for all the work going forward. However, once that foundation is built, the data extraction capabilities will improve over time.

Identifying Entities in Your Data

Powered by machine learning, data extraction software goes beyond the capability of text recognition. Much like human beings, products like AnyExtract.ai have the ability to discern entities within the text itself.

For example, in a real estate deed, OCR may be able to extract 100% of the text from the document, but it will not provide any structure to the extracted text. There will be no paragraph breaks, labels, or any other form of organization.

Machine learning is more advanced in this case. It can not only recognize, but also appropriately label predefined entities within the document. Instead of simply extracting text, it can identify names and specific pieces of data and assign them appropriate labels.

There is often confusion in the realm of automated data extraction regarding the distinction between OCR and machine learning. Although each technology relies on the other, there are important distinctions account for when seeking a data extraction solution that aligns with your needs. OCR is efficient and well-suited for simple documents with minimal structural variation, while machine learning excels in identifying entities, handling complex and varied document structures, and continually improving over time.

Additional Resources

AnyExtract.ai was designed to help real estate professionals save time and focus on the work they love by automating the extraction of structured data from scanned PDFs and Excel workbooks. It automates the process of extracting data that is usually entered by hand, such as schedules of real estate owned, personal financial statements, and tax documents, saving real estate professionals time and money. Learn more about the product from the resources below:

· Hear why we decided to launch HelloData.ai: Launch Time!

· Follow us on our LinkedIn page

Property managers, investors, brokers and appraisers all use HelloData to analyze multifamily comps, optimize rents, and increase deal flow.

Nico Lassaux

Data Scientist Nicolas Lassaux, with expertise in real estate analytics, was pivotal at Enodo and Walker & Dunlop. Co-founder of Hello Data, he's elevating real estate decisions through innovative data use. Passionate about running, cycling, and music.

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