How OCR is applied at LISDATA
OCR (Optical Character Recognition) is a technology that makes it possible to extract text from images. At LISDATA, we use OCR in our day-to-day work to improve efficiency and accuracy in the management and classification of documents and emails.
By Alicia Grande
How we use OCR at LISDATA
OCR enables us to extract relevant information from attached documents and, by combining it with Natural Language Processing (NLP) techniques, we achieve accurate email classification. For example, if an email mentions “I am attaching the invoice,” OCR can identify the document and, together with an analysis of the email body, determine that it belongs to the Administration department. This integration of OCR and NLP provides two complementary sources of information, significantly improving the accuracy of the classification system.
In our R&D department, continuous learning and trial and error are fundamental; they are, practically, part of our DNA as a company. We constantly test the latest models and technologies, adapting them to our specific needs. We have made significant progress by integrating OCR with advanced NLP models, enabling us to extract and analyze information without having to train new models from scratch.
A practical and rewarding project
The Digia project has been crucial for performing sentiment analysis and generating thematic summaries of emails. This not only improves the organization and prioritization of information, but also optimizes administrative and management processes. The ability to validate summaries with language models is particularly innovative and efficient.
The most rewarding part of the project has been combining OCR and NLP technologies to create innovative solutions. For me, seeing how we use OCR at LISDATA and realizing how many ways we can use it to solve complex problems is especially exciting.
What the future of OCR looks like
In my opinion, OCR will not only gain relevance, but will become an essential tool for business digitalization. The ability to convert physical documents into editable data will position companies to leverage advanced data analytics and artificial intelligence (AI). As more organizations seek to digitize their archives, demand for OCR will increase, making it easier to manage, search, and store information.
In our case, OCR has transformed the way we manage documents and emails. Integration with NLP and the ability to adapt to different scenarios without having to train new models have enabled significant advances. This means that at LISDATA we are at the forefront of this technological revolution, positioning ourselves as leaders in the digitalization and optimization of business processes. It is a truly exciting path, and I am very pleased to be part of it as an AI professional.