Templates Vs Machine Learning OCR
Over the past 15 years, I have had the chance to work with many OCR tools and one thing I can say with certainty is that the text extraction quality of these tools has steadily improved with ongoing improvements in artificial intelligence and machine learning OCR techniques.
More than ever businesses are trying to derive useful insights and meaning from scanned images and documents. For example, banks are wanting to extract intelligence such as parties involved and contract expiry dates from scanned contracts, insurance companies are wanting to detect fraudulent receipts submitted during the claims process, and many more. Use cases like these require unstructured text to be converted into structured meaningful data during OCR or post OCR.
OCR tools inherently lack the intelligence to parse or understand extracted text beyond just extracting it. To assign meaning and structure to the content, another system needs to process the extracted text and extract entities and entity types from it.
In this example, the OCR system does an accurate extraction of text however it does not have the intelligence to identify the specifics of the merchant name, merchant address, or other important details such as tax, total, and individual line items.
In this article, I want to compare two post-OCR text enrichment techniques. One is the conventional technique of using templates while the other is the modern approach of applying machine learning.
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Let’s dive into templates first. Templates are what the name says, templates. In this method, the user manually marks co-ordinates for the text of interest on the image and subsequently uses the output of the OCR engine to locate and extract text. This approach works well and is highly accurate if the text layout within the scanned image matches the layout coded in the template.
However, this approach starts to fail for systems that deal with a large number of document layouts and for systems that frequently encounter new types of documents. An invoice processing system that receives new types of invoices from different suppliers is a good example. For an invoice processing system, a template approach may work fine initially but will soon become unmanageable as the number of suppliers grows and change.
Now let’s consider the alternative machine learning approach. A machine learning OCR uses a trained model which encodes thousands of rules for determining the meaning of the content. This model is generally trained using a combination of supervised and unsupervised learning methods. For example, one approach for training could be to use feature data set as follows to predict if a line in the text contains a merchant name.
A trained model can fine-tune itself as more training data is collected and ingested into the training process. The machine learning approach is much more scalable across languages and across different types of documents even if they are not processed by the system. Although this approach requires that initial effort to build high-quality training models and entity recognition models, but once built, this approach scales faster and better than the templates approach.
At Infrrd we are researching and experimenting with various techniques involving machine learning to improve content enrichment post-OCR text extraction from different document types such as receipts, invoices, contracts, and shipping labels.
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