Even in this age of digitization, numerous companies rely on paper documents to store data. According to research, US businesses alone use 12.1 trillion sheets of paper a year. Due to this, employees can face problems like loss or misplacement of important documents. Moreover, deriving useful information from the copious amount of documents, both in online and offline form, can be time-consuming. In this fast-paced world, such issues may lead to hindrances in the functioning of a business. A solution to these problems is using the latest technology, like Intelligent Document Processing.
Intelligent Document Processing (or IDP) allows enterprises to extract and process data from various online and offline documents. It can sort and store online documents (like PDFs, Word files, etc.), as well as convert paper-based documents into machine-readable documents.
Components of Intelligent Document Processing (IDP)
Intelligent Document Processing (IDP) uses applications of AI like Machine Learning (ML), Computer Vision, Optical Character Recognition (OCR), and Natural Language Processing (NLP) to store, convert, and present document data. It effectively helps companies save time and money while increasing data processing accuracy.
1. Machine Learning (ML)
Machine Learning is a branch of Artificial Intelligence (AI) that helps computers to process data and learn to predict the right output over time. It uses complex algorithms to process enormous amounts of data and provide the right result with increasing accuracy over time. Computers can extract and process the right data without supervision. ML in IDP can efficiently help enterprises tackle human error caused while processing data.
2. Computer Vision
Computer vision uses algorithms to understand patterns and accurately derive meaningful data from images, videos, and other visual inputs. According to research, the computer vision market is projected to reach $41.11 billion by 2030, registering a CAGR of 16.0% from 2020 to 2030. The major benefit of computer vision in IDP is to cut down the time required to convert abundant unstructured data into structured one. It can help companies to classify documents efficiently and extract the necessary data from visual inputs.
3. Optical Character Recognition (OCR)
OCR systems allow computers to convert text from various sources like PDFs, Word Files, and even paper-based documents into machine-readable data like JSON. This massively reduces the manual effort required for data entry. OCR technology has been in the market for a considerable time. However, the introduction of technology like machine learning has allowed us to boost its capabilities to extract data from complex documents efficiently.
4. Natural Language Processing (NLP)
While OCR allows computers to extract and convert documents into machine-readable data, natural language processing technology allows it to understand the meaning of that data. Human language is full of intricacies like grammar, idioms, and more. NLP helps computers to perceive and recognize text and spoken words as humans do. In the case of IDP, natural language processing can help companies extract meaningful data from emails, reports, and other documents.
Conclusion
In today’s fast-paced world, technologies like Intelligent Document Processing (IDP) can help enterprises to cut down redundant work by avoiding outdated methods like manual data entry. Apart from automating the documentation process, it also ensures the accuracy and quality of the data is maintained.
