UiPath Document Understanding
Basics of Document Understanding for Beginners
In today’s world of business, it is no surprise that companies are dealing with an ever-increasing amount of paperwork. This results in a set of monotonous tasks which are not only time-consuming but also reduce productivity. In this article, we will see the UiPath Document Understanding framework as a perfect solution for companies seeking an effective and streamlined approach to automate document processing. By leveraging the power of artificial intelligence (AI) and machine learning (ML), the UiPath Document Understanding framework ensures that all your documents are processed efficiently and accurately. With the UiPath Document Understanding framework, you can take control of your documents and focus on other important aspects of your business.
Have you ever wondered how much time staff spends in processing these documents? It is worth noting that processing different types of documents can be challenging due to their varying structures. Some documents, such as forms, passports, and licenses, have a fixed format and are, therefore, easier to handle. However, other documents such as agreements, communications, and medical records may not have a specific structure, making their processing more complex.
Intelligent document processing improves productivity, accuracy, customer and employee satisfaction, and compliance.
The robot, which is a software agent responsible for handling automation, gradually becomes an expert in handling complex tasks such as dealing with different types of templates, handwriting, signatures, checkboxes, skewed or rotated documents, various file formats, and low-quality scanned documents. This is achieved through artificial intelligence models developed by continuous training. With UiPath robots, these difficulties and more can be easily overcome. Humans can occasionally check their performance or manage exceptions as needed.
Understanding and interpreting information from various document types and formats is known as document understanding.
Types of Documents in Document Understanding
There are primarily following three types of document in document understanding:
- Structured: Structured documents are designed to collect data clearly and concisely, often using forms with specific sections for each piece of information. Examples of these types of documents include tax forms, questionnaires, and surveys. These documents typically consist of tables and key-value pairs, allowing for easy organization and analysis of the data collected.
- Semi-structured: Semi-structured documents do not have a rigid structure like structured forms. They are not limited by predetermined data fields. Although they do not follow a fixed format, they adhere to a common one. Generally, data is found in key-value pairs, but paragraphs may also be used. Examples of semi-structured paperwork include healthcare lab results, invoices, receipts, and purchase orders.
- Unstructured: When information is not organized in a clear and structured manner, it is considered unstructured content. These kinds of documents are easily understood by humans but can be quite challenging for robots to process. Examples of unstructured papers include contracts and annual reports. Although some of them may contain tables and key-value pairs, most of the data is in an unstructured format within the text.
OCR vs Document Understanding
Intelligent OCR is the UiPath Studio package that includes all the tasks required to enable information extraction. The full Document Understanding Framework is enabled by this package. With the use of OCR, text may be read from images while each character’s location is identified.
OCR is a technique that aids in the digitization stage of the Document Understanding process, despite having the same name but different capabilities. Full document processing is made feasible by the process of Document Understanding, which is made possible by UiPath’s Intelligent OCR package.
How Does Document Understanding Work?
UiPath Document Understanding robots using artificial intelligence (AI). For documents with fixed structure, At the same time, documents with varying layouts or with no fixed structure require advanced AI skills which can automatically determine the location of data even if the layout changes. Machine learning (ML) models are continually improving robots’ skills to make them fast and accurate at document processing.
Document Understanding Framework
The Document Understanding Framework was created to assist users in combining various strategies to extract data from numerous documents not always with the same structure.
On a high level, documents go through five fundamental steps: defining the types of documents and the data to be extracted, providing the text and its location, classifying the documents from the given list (in the first step), extracting the information, and human verification of the extracted data.
- Taxonomy: The first step is to classify the documents and provide the data you wish to extract. Work with contracts, ID cards, invoices, government paperwork, employee resumes, and other papers simultaneously. You can include several document kinds and the fields you want to extract in this preprocessing stage.
- Digitization: The process of digitization is carried out on each individual document. The requirement to use your preferred OCR engine applies only to non-digital (scanned) documents. The Document Object Model and a string variable holding the entire document text are the steps’ outputs and are forwarded to them. While OCR is not the same as Document Understanding, you still need to use an OCR engine. One is expected when working with scanned documents or images. The OCR engine will be used only if the incoming documents require OCR processing, and the decision gets taken on a page-by-page basis. Fortunately, applying the Framework means using a single activity to digitize both scanned and native documents.
- Classification: After digitization, the document is classified. Identifying the type of document the robot is encountering is the next stage if you’re working with multiple document types after the papers have gone through digitization and been converted into a format that the robot can read. In essence, the classifiers receive the document text and object model produced by the digitization processes and report the types they find in the incoming file. It’s crucial that you’re able to configure the classifiers, utilize several classifiers in the same scope, and train them later on in the framework. Implementation of the appropriate extraction approach is aided by the classification results.
- Extraction: Extraction is getting just the data you are interested in. In this stage, you will extract the required data from your document. You can design your own extractor or use one of the extractors included in the Intelligent OCR package. In this framework, you can use different extractors, for the different document structures.
- Validation: Through the Validation Station, a human user can verify the extracted data. A human can validate the extracted data. It is always advised to use human validation to ensure complete accuracy of the results. The Present Validation Station activity initiates this stage.
- Export: Once your data has been verified, you have the option of using it directly or saving it in a DataTable format that can be quickly transformed into an Excel file. To process the data further in Queues, enter the data into an ERP tool, or carry out additional tasks as specified by the document processing workflow, you can either copy the information to an Excel or a Datatable.
- Training Classifiers and Extractors: Classification and Extraction are as efficient as the classifiers and extractors used are. If a document wasn’t classified properly, it means it was unknown to the active classifiers. The same way goes for incorrect data extraction. The Framework provides the opportunity to train the classifiers and the extractors, to improve recognition of the documents and fields.
Conclusion
The goal of Document Understanding is to enable you to integrate several strategies for information extraction from various document types. The major objective is to create a single workflow that would extract data from various documents while making the data extraction procedure as simple as feasible.