ZigaForm version 5.7.1

Taking intelligent document processing to the next level

Taking intelligent document processing to the next level

Intelligent document processing (IDP) simplifies work and saves time and cost for millions of people who work with all sorts of documents. Here are some questions that you might have about IDP answered by Sathish Kumar Babu, AI Engineer and lead of Docketry, an IDP solution developed by Nuvento.

 

Understanding intelligent document processing (IDP)

Intelligent document processing has revolutionized how industries handle their documents. Let’s get to know how IDP gets things done and what’s next.

 

What is the difference between automated Optical Character Recognition (OCR) and Intelligent Data Processing (IDP)? 

Automated OCR is requirement-specific and relevant extraction fields of any document are specified and sent to their downstream. But IDP recognizes the document without us having to mention the document type.

 

What’s next in IDP? 

Learning from each document type so that the ML algorithm automatically picks the key information without mentioning the keywords explicitly.

 

How is IDP making data processing easier? 

Reducing the manual effort of the end user is the ultimate goal. The user does not have to segment or categorize the document here and the document processing with a more confidence score and better processing speed.

 

Building Intelligent Document Processing solutions: Underlying technology and algorithms   

IDP uses advanced AI/ML models, NLP, computer vision, OCR and other technologies to extract data from complex documents. Let’s get to know the technology, challenges and innovations in IDP.

 

What are the most common algorithms used in OCR?  

Algorithms are purely dependent on the business study as the same algorithm won’t work for all the use cases across the domain. But classification models can help to distinguish the key fields of the specific document type of any domain.

 

Is unsupervised learning an effective approach to building OCR/IDP models? 

Yes, we can opt for clustering to identify the common related information in any document. Identifying hidden patterns or data groupings without the need for human intervention. But for classifying the document type, you need supervised learning.

 

How can companies like Nuvento compete with industry giants like Google and Facebook with access to huge datasets in developing AI/ML solutions? 

Data collection is the toughest job, and a lot of money is invested by tech giants as part of R&D. We should accept the fact that we rely on them for data gathering for any business study. Still, fine-tuning the data/pre-trained models and making it easier for any AI solutions is where the skill comes and Nuvento is not too far away from them.

 

How do you overcome bias in AI/ML algorithms? Did you have a challenge like that with Docketry? 

We generally overcome bias by complexing the model, like training a model with a larger data set. But for Docketry that was not what we did. Currently, we are using the pre-trained model and customizing it according to the requirement.