Automation of document processing with the help of artificial intelligence is of increasing interest to companies dealing with a huge number of documents every day. Let's simplify the concept of AI-driven intelligent document processing into several stages and explore how a practical AI data management system can be structured.

Before we can properly train AI to process our documents, we first need to improve the quality of the data we feed to OCR and NLP models. This is called data preprocessing. The first part of this technique is to deskew the data by correcting the angle of the scanned images. Denoising is then used to remove visible defects. After that, binarization converts the images to black and white. Finally, the images should be cropped.

Once preprocessing is done, it’s time for AI algorithms to begin inspecting, evaluating, and classifying the data. The AI will assign labels to fields. We can also combine some fields into entities that relate to different business tasks.

A critical component of this process is data normalization. By transforming the data into a common format, it becomes easier to analyze with AI tools and makes the data more accessible for end users. For example, a street address is an important bit of information for some businesses. However, human error often leads to inconsistencies between documents when these addresses are written. Normalized data should be easy to search or use for creating reports and filtering data.

If the data isn’t stored locally but instead on third-party external databases, a company could handle this by creating an “over the top” software layer when the data can’t be directly interacted with. The process would start by first identifying the data’s source. Then, it would be copied so that it can be interacted with in that topmost software layer. Then, the data can be operated on by AI algorithms or other tools.

If data is imperfect, corrupted, or presents some other kind of outlier, it will pose problems for an algorithm. To approach this, a separate AI algorithm that is specialized for detecting anomalies can be used to identify data that deviates from the general pattern of the set.

After the anomalies are identified, a data analyst should examine the deviations to validate the results. This expert should be able to make manual corrections to the data since there might be domain-specific factors that impact the quality of the data.

The final step of the AI data management flow for document processing is to take a closer look at the bigger picture. There are three major aspects to pay attention to once the data has been processed by the AI algorithm:

Data analytics can be generated manually or on a weekly or monthly basis. Analytical software is typically created as a separate project. However, it’s still the natural next step after you’ve properly processed your business’s documents.

There are enough ready-made solutions for document processing on the market, so why is there still a demand for custom solutions? If a business is dealing with a large amount of paperwork that must be handled digitally, and many of the documents are unique, the capabilities of existing software may not be enough.

To succeed with a custom solution, you’ll need to set an effective data management pipeline for each specific case. Having a custom solution will also give a company the advantage of being able to create integrations with other software components more seamlessly. This allows businesses to get a more robust and secure ecosystem for data transmission.