Cognitive document automation uses a variety of functions such as natural language processing (NLP) and machine learning for clustering, classification, isolation, OCR, extraction and (human language) interpretation of any kind of documents.
Machine learning is a key component of CDA, facilitating CDA device setup and maintenance. Just provide a few examples of each type of document, and CDA automatically understands how to define and extract data from them— there is no need to write rules for each type of document or construct static layout-based models. When records change over time, machine learning gracefully responds without human labor to those changes.
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.
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. Sometime 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 error such as typo error or missed up some 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 to get data from pdf for at least 2hours from data extraction. see more our article
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