Cognitive document automation employs diverse functions, including natural language processing (NLP) and machine learning, to cluster, classify, isolate, perform optical character recognition (OCR), extract information, and interpret human language from various types of documents.

Read About: 5 Easy Hacks to Improve OCR Accuracy

Machine Learning

Machine learning is a key component of Cognitive Automation, facilitating Cognitive Automation device setup and maintenance. Simply offer a handful of examples for each document type, and Cognitive Automation will grasp how to define and extract data from them. No need to create rules for each document type or build static layout-based models. As records evolve, machine learning adeptly adapts to changes without requiring manual intervention.

When paperwork gradually evolves, standard record capture technologies are obsolete soon after day 1 of development activities, requiring constant manual adjustment effort to keep up.

Extracting Data with Cognitive Automation

Transfer data from pdf format such as e-bill/e-invoice is a very time-consuming process as it needs the user to switch between pdf reader and excel multiple times. Sometimes there will be more than 20 PDF to do and each pdf has more than 10 pages. This work process will require at least 3 hours per day. The user also needs to do a check between pdf and excel as to spot human errors such as typo errors or missed up information.

However, Gleematic has helped to solve the challenge. It is able to read the pdf and extract all the information that the user needed to excel at a faster speed. Gleematic has the A.I. feature to understand the unstructured data and capture the data. It saves the error which will possibly cause by the human.

Users will be free-worry and have more time to do other work as Gleematic has helped the user to save the time extracting tables or any kind of data from pdf for at least 2 hours from data extraction.