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• Machine Learning vs OCR: Which is Better for Data Extraction?

  When considering options for automated data extraction, there’s often confusion around the differences in all the technological options available. Specifically – between Optical Character Recognition (OCR), and Machine Learning (ML). 

  And no wonder there is confusion. As technology improves, the lines between OCR and ML are becoming more blurred. OCR is an important component of machine learning, and machine learning is often an important part of improving OCR results. 

  However – if you are trying to work out which is best for your business today, there are some important distinctions between the technologies to consider.

  What is Optical Character Recognition? 

  Optical Character Recognition converts scanned images of text into machine-encoded text. This is done by scanning the image and then using an algorithm to identify the characters and their location on the page. The OCR software converts these characters into a digital format that can be edited, searched, or manipulated in other ways.

  The OCR process has been around for decades but it has become more popular with the rise of digitization and data extraction. These days, modern OCRs are leveraging advances in machine learning to improve text recognition ability with the help of neural networks. For example, you feed a neural network a line of text. It can process a letter at a time, considering what came before it and what occurs after it, to predict what the character is (even if the character is 90% eroded).

  What is Machine Learning?

  Machine learning is a subset of artificial intelligence that provides computers with the ability to learn without being explicitly programmed. ML algorithms are used in many different fields, but they are often used in the field of computer vision and natural language processing.

  When machine learning is involved in the data extraction processes, it often includes OCR to help with the text recognition. However, part of ML’s rules-based approach allows for capabilities well beyond simple data extraction. One important difference is that with ML, the OCR can be fine-tuned to your specific problem or document – beyond just recognizing text on the page. ML can also identify and classify key information found in the text. 

  For example – while OCR technology can give you the letters MADISON SMITH, ML technology can help you identify that those letters make up a person’s name, and in a specific document (say, a property deed), identify that they are the Grantor listed on the deed. And the more documents processed, the better the technology will become as the machine continues to learn.

  Which is better for data extraction?

  The answer depends on what you need.

  Complex vs Simple Documents

  Optical Character Recognition shines if you are extracting data from one type of document; the simpler the better. Think invoices, receipts and other templated documents that have very little variation in their structure. 

  However, OCR technology struggles with complex documents. In part, because OCR technology relies on patterns that are easily broken when trying to extract data from some more complex documents, forms, and other files. Format changes are not handled well by OCR. 

  Meanwhile, machine learning data extraction works on any document. ML especially shines with complex documents that have a great deal of variation. It can also handle all the types of documents you want to use. 

  A common point of confusion is that machine learning uses 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 be a strong foundation for all the work going forward. However, once that base is established, the data extraction will get better over time.

  

  Entities in your data

  Machine learning powered data extraction software goes far beyond the capabilities of being able to recognize text. It can identify entities within the text itself.

  For example, in a property deed OCR can pull out all the text from the document. However, it will not structure the extracted text. There will be no paragraph breaks, labels, or anything other than the text itself. 

  On the other hand, ML can recognize and label predefined entities within the document itself. It won’t just pull text, it will recognize names and specific pieces of data, and label them appropriately. The example below shows the difference between the two outputs. 

  OCR and Machine learning are often confused in the world of automated data extraction. While each technology does rely on the other, there are important differences to consider when looking for a data extraction solution that fits your needs. OCR works fast and well for simple documents that have very little variation in their structure. Meanwhile, machine learning can recognize entities, handle complex and varied document structures, and improve over time.

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