Document Annotation, Its Industry Significance, and Best Practices

By images and document annotation, we mean the process of identifying fields and values within a document in order to extract useful information. By using AI and machine learning algorithms, data sets in files can be categorized and extracted automatically without the need for manual intervention. When you mark up documents, you can easily find the information you are searching for without having to comb through the entire document. 

Data can be structured and shown in a clear and comprehensible format that other users can understand and appreciate as well. Image annotation traditionally is handled by specialized staff, with innovations in technology, OCR, and RPA eliminating the need for employees to look up information in documents manually and freeing up workers for more productive tasks. 

What Is Document Annotation?

Data annotation refers to the tagging and organization of information in a way that makes it accessible for future analysis and provides valuable insights. Obtaining information cannot be accomplished without first finding it, so annotation is the first step before data extraction.

The number of instances of the value 'city' in a novel is an example of document annotation in the real world. You can perform a scan of paragraphs of text and locate the frequency of a particular field by using document annotation. Similarly, document annotation technology can be applied to pay slips, invoices, purchase orders, receipts, and other documents to locate valuable information.

Importance of Document Annotation

Automating document annotations and streamlining business processes are used by numerous industry verticals. Annotating images and documents is crucial to processing complex information, whether in educational institutions, public corporations, logistics, or supply chain operations. 

By finding and assigning the appropriate line items and key-value pairs to a document, annotations help verify and validate the information. It allows for cross-referencing past information, identifying details, and mapping out data. Accuracy in document & image annotation is important as it determines how efficiently organizations can run their business operations without delays and downtimes possibly occurring due to erroneous data interpretation.

Best Practices for Document Annotation

When looking for document processing services in the market, look for an expert annotation company that abides by standard annotation practices. The ideal approach for document and image annotation is to abide by the best industry practices and the document annotation experts having gained mastery of image annotation and labeling are very well aware of it. Irrespective of the document file size and the document type, they can put in the best practices to come up with high-quality annotation for documents.

Here are the best practices for documents annotation to follow while processing documents and images for annotation:

1- Remain Persistent with the Process

Being persistent is the key to quality document annotation. First, develop familiarity with the industry use for the annotated images and documents before putting the documents for further annotation process. Ensure that APIs are trained on datasets and given a manual guide for the first few instances to allow them to recognize file structures. Once an AI algorithm identifies key information in a document, it takes over the process. 

2- Lay Out an Ideal Extraction Outline

Annotating documents should be preceded by correctly establishing the data extraction outline. All key-value pairs are adjusted, and the appropriate data types are assigned before annotating. As a next step, divide the document into sections and order key value pairs properly. 

3- Set Up the Data In Tabular Format for Easy Organizing

The use of tables enables us to organize information more conveniently during annotation. Lists and tables can be created by setting rows and columns. API models differ, and each has a different set of tables. 

4- Employ Human-in-the-Loop Approach for Annotation

Annotating and mapping data from large datasets requires the involvement of multiple hands. Those with more experience with document and image annotation can provide feedback to those with less experience and help improve projects. After completing an annotation, ask peers to review it manually. Automated document annotation becomes seamless when this is done, and none of the details need to be read. 

5- Also, Use AI-Enabled Document Annotation for Fast Process

In the document annotation process, you must assign key-value pairs to your documents. You can save the changes once you have trained the API to annotate your images and documents. When you have set up these API models correctly, they will reduce processing times automatically.

Instead of bulk processing documents, care about the quality of your annotations. Once the API has accurately annotated your initial samples, you can use it to annotate documents in multiple batches.

Conclusion

There are numerous annotation experts in the market to offer you high-end industry-specific document processing services but be wise when picking the one for your application. An expert document annotation company uses intelligent OCR and AI to automatically annotate and extract data from documents for users and eliminate the need for manual data extraction. The software can find and extract data from semi-structured documents as well.