Document analysis aims to extract data accurately and quickly. It is located at the intersection of document processing and artificial intelligence (AI), which together are creating a future where almost anything can be automated.
Its ecosystem includes technologies that can interpret the information and meaning of various documents, including handwriting, checkboxes, and stamps. Machine Learning (ML), on the other hand, stimulates continuous innovation. In fact, it has made document analysis one of the fastest-growing areas of automation. So in this article, we take a closer look at the technologies that make up this ecosystem and analyze the benefits of our solution: the Smart Repository.
Optical character recognition (OCR)
OCR converts images from typed, handwritten, or printed text In one coded text by a machine that can be further processed to extract the desired data. The technology also extracts information about layout and structure of the content.
You may have been slowed down, especially when working with PDF documents where you couldn't copy text or search. Indeed, PDF pages are essentially images. Likewise, you can have a scan, photo, or screenshot of a receipt in specific graphic formats: JPEG or TIFF. OCR can then easily gather all the necessary information from these files, without the need for a person to read all the documents by themselves.
Model-based extractors (TBE)
The TBE (Template-based extractors) extract data using fixed rules that are applied to templates created by a user or a machine. Therefore, TBEs may not work for documents whose structure changes frequently or which require different model variations. This technology is therefore perfectly adapted to managing a relatively small number of stable document templates. When a change in document format is required, it is easy to change the template manually.
There are a lot of providers offering TBEs. When evaluating which solution to choose, you should pay attention to how easy it is to set up a model. In fact, some of the best companies offer technologies that create models semi-automatically using a human process in the loop that only confirms the choice.
Machine learning extractors based on supervised learning (SMLE)
The SMLE (Supervised-learning-based machine learning extractors) can be used for structured and semi-structured documents. Invoices and purchase orders are a good example. SMLEs work by labeling a set of sample documents, that is, by associating the data elements to be extracted with the area of the document from which the data is extracted.
Unsupervised learning (USL)
This technique consists of analyzing a set of data without pre-labeling. Unsupervised learning uses pre-trained models or different representations of knowledge to deal with unstructured documents. Common use cases include analyzing financial statements, contracts, and emails.
Natural Language Processing (NLP)
NLP technologies help computers understand human language. As such, he is often combined with other technologies to complete a series of tasks. It allows organizations to perform text analysis, extract entities, and automate processes by setting intent in unstructured documents such as emails. In addition, it may beAnalyze the feeling of a text - in other words, to define whether it is positive, negative or neutral. This can be particularly useful for interpreting news, social media, or correspondence content.
Our solution: the Smart Repository
Our Smart Repository is based on the latest technologies of automated language processing (TAL/NLP) in order to extract the essentials from the company's entire intellectual capital. AI-assisted research provides intelligence and analysis semantics that makes it possible to understand what users are looking for. The Smart Repository, thanks to themachine learning, will continue to learn over time. If your industry or business uses specific jargon, we'll understand it without you telling us.
Directly from their work environment (for example PowerPoint or Word), users can instantly access the most relevant business data. Even better, this does not require any pre-tagging of documents, the AI will ingest unstructured data and discover relationships by itself!
About StoryShaper:
StoryShaper is an innovative start-up that supports its customers in defining their digital strategy and the development of automation solutions tailor-made.
Sources: StoryShaper, UiPath
