Document annotation refers to the process of identifying fields and associated values in a document and extracting relevant information using a set of criteria. Machine learning algorithms and AI technologies are used to classify data sets in files and extract data without requiring any human intervention. Annotating documents makes it easier to sort through and find specific information without needing to go through entire documents. It also makes it convenient to structure data and showcase it in a presentable format that is widely accepted by other users. Organizations hire dedicated specialists for document annotation but with innovations in document annotation technology, OCR, and RPA, companies no longer need employees to manually look up data in files and focus on more productive tasks instead.
Document annotation involves labeling and organizing data in a way that it delivers key insights and makes it approachable for further analysis. The step before data extraction is what is referred to as data annotation because information cannot be extracted unless it is found first.
A classic real-world example of document annotation would be locating the number of instances of the value ‘city,’ in a novel. With document annotation, you can scan through paragraphs of texts and find the frequency of that field. Likewise, you can apply document annotation technology to locate key information in payslips, invoices, purchase orders, receipts, and other document types.
Document annotation automation software is used by various industry verticals to digitize and streamline business processes. From educational institutions, public share trading companies, logistics, and supply chain operations, the importance of document annotation cannot be ignored when it comes to processing critical information.
Document annotation helps verify and validate information by finding and assigning the appropriate key-value pairs and line-items in a document. It is used for mapping out data for authenticating details and cross-referencing past models. Successful annotation is needed so that organizations can run their business operations efficiently without suffering from any delays or downtimes due to erroneous data interpretation.
The following are some of the best practices to use when annotating documents:-
Consistent annotation is more important than correct annotation in 90% cases. Train APIs with datasets and enable them to recognize file structures by manually guiding them for the first few instances. AI technology and smart machine learning algorithms take over the process once you’ve helped it identify key information in documents.
Your extraction schema should be properly set up before you start annotating documents. Make adjustments to key value pairs and ensure that all fields have been assigned the appropriate data types before you begin to annotate. Create sections for different parts of the document and make sure your key value pairs are ordered correctly.
You can use tables during data annotation to make it convenient for organizing information. Set up rows and columns for making lists and use tables to make a summary of data. There are different annotated examples you can find under Docsumo’s APIs & Services modules to get an idea of how to go about this. Every API model is different and each one uses tables in their own unique ways.
When dealing with large quantities of datasets, it is important to involve a number of people in the data mapping and annotation process. Users with more experience in annotating documents can provide feedback to less experienced ones and improve projects.
Request your peers for a manual review after you’ve done your first data annotation. This can help you refine the process and make automated document annotation seamless. You don’t have to read all the details, just the ones which are important.
Once you have trained the API to annotate your documents, you can save the changes and finish creating API modules. The document annotation process involves assigning key value pairs and this is the critical part. Your API models will take over and automatically reduce processing times when you’ve set up this process right.
Focus on the quality of your annotations instead of trying to bulk process documents to make automation smoother down the line. You can apply the API to annotate documents in multiple batches once it accurately annotates your initial samples.
Docsumo uses intelligent OCR and AI to annotate and extract data from documents automatically for users and removes the need for manual data extraction. The software can find and extract data from semi-structured documents as well.
To experience this firsthand, sign up for a free demo with Docsumo and watch how we make your document annotation experience hassle-free!
In today’s dynamic business world, filing and archiving official documents in the digital form makes it handy, and works wonders in the future or in unforeseen circumstances.
Optical Character Recognition (OCR) is the technology to convert an image of text into machine-readable text. It is the underlying technology for various data extraction solutions including Intelligent Document Processing. However, OCR is not smart enough to figure out the context in a document - it works simply by distinguishing text pixels from the background and finding a pattern. This limitation could cause inaccuracy in captured data that could directly impact the output of your data extraction model.
Accounts payable is a key financial function for any business. Corporations can have thousands of suppliers; even for relatively smaller businesses, the number of suppliers could be in hundreds. All the invoices they receive from these suppliers come in multiple formats, layouts, and templates - some semi-structured, some unstructured. Therefore, firms expend time and resources to capture invoice information through manual data entry and verification of accounts payable. Manual data entry is not feasible in the long run, definitely not on a large scale. Before we talk about how intelligent invoicing solves the problems associated with manual invoicing, let’s discuss the challenges in much detail.
As most of an organization's information is available in an unstructured format, processing it requires an automated system that can handle documents with minimum human interaction. OCR is one such technology, but its scope is limited as it requires human interaction and is highly dependent on the layout and structure of the document to be processed.These limitations are overcome by Intelligent Data Extraction.Using artificial intelligence, the Intelligent Data Extraction technology extracts data from documents and transforms it into useful information through the extraction process. It functions as a singular tool for extracting information from any type of document and aids in optimizing company operations.