There are a good number of operations in which companies need to process the data extracted from all types of documents and work with them. Information that they must store or integrate for use in their internal systems. The use of artificial intelligence technologies - such as computer vision, natural language processing, or machine learning - will make AI Document Extraction processes much more efficient and secure.

When launching technologies related to digitization and automation, one of the obvious areas for improvement is the search for efficiency. In all this vortex of speed, of reducing time-to-market to be more competitive, there are some processes where people are still smarter and faster than machines, one of them is document recognition.

Companies continue to receive thousands of certificates, credentials, or deeds from different sources and in multiple formats, which employees must receive and classify, and then perform a series of actions based on their content.

Current OCR technology has been in charge of converting all types of scanned documents, photographs, etc. to editable text. But it must be borne in mind that this technology works as long as a series of conditions are met. Have good quality at the source, with easily recognizable and perfectly aligned characters. The text must be structured or semi-structured to allow pattern extraction.

This technology allows you to perform basic extraction tasks based on text or template patterns that configure certain areas of the document.

When reading a document, people follow a series of steps. Generally, we perform a spatial composition of the page to identify the points where the information can be. Then we focus on each of these areas to extract relevant information, the one that catches our attention (bold, etc.). Finally, we go on to identify the extracted values ​​or assign a context to them.

Intelligent document processing seeks to simulate human behavior in the processes of extracting relevant data from documents, using artificial intelligence techniques and tools.

The first one is computer vision. It is a technology that allows images to be acquired, processed, analyzed, and understood so that they can be processed. Using powerful image processing tools and algorithms, it manages to prepare documents so that noise and imperfections disappear, thus facilitating the recognition process.

To achieve this, parameters such as quality, contrast, or zoom are adjusted until the optimum degree of “readability” is reached. This is a key step and the rest of the process depends on its correct application. This is what will ensure the success of the extraction.

This phase requires high computing power, necessary for image processing, especially when it comes to high-quality or large files. Efficiently performing this step will ensure adequate response times for the entire process.

In the next step, that of classification and extraction is the one in which artificial intelligence is applied to search - within documents - for certain text or visual patterns to identify the different types of fields.

The normalization phase is supported by a self-learning model, which allows the system to increase the quality of the extractions with formats similar to the standard one.

Author's Bio: 

Quickly capture, extract & analyze data from large sets of documents with AI & Machine Learning.