Ever-growing volumes of unstructured data stored in countless document formats significantly complicate data processing and timely access to relevant information for organizations. Without proper optimization of data management workflows, it’s difficult to talk about business growth and scaling. That is why progressive companies opt for intelligent document processing powered by artificial intelligence.
How AI address the key challenges of document processing
Despite the fact that digitalization has been a top priority for businesses in recent years, companies still spend millions of dollars on manual document processing. According to statistics, about 80% of the data generated by organizations is unstructured. Moreover, this extends to various document formats, including spreadsheets, PDFs, images, etc., which require different approaches to processing this data.
Manual data processing approaches are not only subject to errors but they also could lead to losing important documents, problems with version control, and various legal and regulatory risks. Incorporating AI technologies into the data processing workflow can help to reduce these challenges. AI app development allows for the automation of the classification and extraction of unstructured and semi-structured data with a high level of accuracy.
There are several options for implementing artificial intelligence for document processing that meet different business goals, made possible by AI’s ability to find hidden patterns beyond the reach of the human eye.
Data extraction with Machine Learning OCR
Traditional Optical Character Recognition (OCR) systems that are usually used for automated data extraction are template-based and require extensive supervision. While this is an acceptable option for highly structured documents like spreadsheets, problems arise when it comes to files with high variability like invoices, receipts, etc. The implementation of machine learning algorithms allows you to significantly expand the capabilities of OCR and provide more flexibility.
Any OCR algorithm includes three basic steps: image processing, text detection, and text recognition. The introduction of machine learning for the last two steps allows you to significantly improve the output. The end result of processing a file using machine learning OCR is converting the document into structured data for easy processing in your database. Since the accuracy of results with traditional OCR depends a lot on the quality of the original document, ML models could also help with solving this issue.
For instance, ML could help to increase the quality of images by applying denoising algorithms or binarization of the images and other approaches that will be the most suitable for resolving the problem of low quality images.
With machine learning, you can teach the model to associate various shapes with a specific symbol for greater accuracy. Such OCR systems can effectively process more complex data, for example, if you are dealing with blueprints and engineering drawings recognition. Also, machine learning can provide a more complete analysis, because it can analyze not only a certain part of the document but also the entire context.
Integration and customization of ready-made software such as OpenCV and Tesseract OCR allow you to create a solution that will meet all your specific needs. ML-based OCR systems help companies avoid mistakes that result in the loss of important data points and greatly facilitate the process of data management. Also, it significantly saves human resources because machine learning requires less human intervention over time. But it still is great if the data recognized by AI is validated by humans from time to time in order to highlight problem spots of recognition and retrain models on new updated data.
How to classify and analyze documents more effective with NLP technology
Before going to data extraction we need to understand the kind of data we are working on. That’s where natural language processing (NLP) comes to the rescue. Unlike simple rule-based software that can extract information based on strictly defined keywords or tags, NLP is more flexible and can interpret information based on intent and meaning, and thus properly consider changes and options in documents.
Named entity recognition and classification
One of the basic tasks of NLP is Named Entity Recognition, i.e. identifying named entity mentions within unstructured data and classifying them into predefined categories (names, locations, amounts, etc.). Statistical NER systems usually require a large amount of manually tagged training data, but semi-supervised approaches can reduce this effort. For example, sometimes it’s sufficient to use out-of-the-box NLP packages that include pre-trained machine learning models and don’t require additional data for training. If this is not enough for acceptable results and the business uses specific naming, it will be necessary to label additional entities and retrain the NLP model on the updated dataset.
Text Classification helps to categorize text according to its content. For example, it can be used to classify and assign a set of pre-defined tags or categories to medical reports or insurance claims depending on different criteria. Or you can use classification to prioritize customer requests for a customer support team by ranking them by urgency.
Sentiment analysis
Sentiment Analysis is a way to use natural language processing (NLP) methods to identify and extract people’s opinions, attitudes, and emotions from text. It is a common task in NLP. It allows you to define the thoughts and emotions of customers about your products and services from reviews, survey responses, and social media comments. To determine the opinion, the system is usually guided by keywords. For example, “like’ or “love” signal a positive statement, and “do not”, “not” or “hate” a negative one. However, it’s also worth considering the special types of language constructions, because sometimes “not” and “never ” can have the opposite meaning (for example, “not bad”). Also, difficulties can arise with slang. For example, the word “sick” can have both a negative and a positive connotation. Nowadays, it is completely possible to handle these tasks with more advanced deep learning models that are able to understand context from the written text and identify the emotions with a minimum of mistakes.
The accuracy of document processing with NLP depends on many factors, including variation, style, and complexity of the language used, the quality of training data, document size (sometimes large documents are better because they provide more context), number of classes and types of entity, and many more. Each case is unique and requires a customized solution that can be provided by experienced machine learning consultants.
What you need to implement AI-powered document processing
Deciding to integrate AI-powered document processing into your workflow, you’ll face two options: complete automation and semi-automation with human supervision. The first case is possible if your business processes are logical and repetitive. If there’s any chance of variability that can impact the decision-making, it’s better to opt for semi-automation where the human has the final word.
The creation of an AI product like an intelligent document processing system consists of the following stages:
- Identifying a business problem to solve
Different cases require different solutions and the use of AI is not always justified. That is why it’s important to clearly understand exactly what results you want to get from the automation of document processing and to consult with specialists about the means of achieving these goals.
- Choose the right technology
Consulting with software developers will help you choose the best tools to implement your idea. It can be both the customization of ready-made platforms and the development of completely new solutions if the specifics of your project require it.
- Data preparation
To train models, it is important to have accurate, relevant, and comprehensive data. You can have your own databases or find open-source datasets, as well as use web scraping tools. Then, if necessary, the data is cleaned and processed by removing errors, formatting, and handling missing values.
Once the development team has the data they need, they can build and train the models, as well as improve them. The critical point for business owners in this process is finding a reliable development partner who has the necessary expertise and is able to match business needs with technology capabilities. With real experts on your side, you will be able to implement intelligent document processing without additional complications and personally experience the benefits of using AI to optimize business processes.
