As per Statista, by 2025, the overall volume of enterprise data will have doubled globally, reaching more than 2 petabytes.
Additionally, 80% of this data will not be organized or structured. Although unstructured data are unquestionably valuable sources of information, processing them manually is time-consuming and inefficient.
Therefore, how can businesses use their data to produce actual business value while not burdening their staff with the additional workload?
The answer is by using document AI technology.
Key capabilities of AI in document processing
Let’s understand how automation technology helps with document processing:
- With the help of document AI technology like NLP (Natural Language Processing), data can be collected automatically from documents, and the content can be easily comprehended.
- Intelligent document processing fetches data from unstructured and semi-structured documents and formats and converts it into structured data. Furthermore, using AI, this data is extracted accurately.
- Data can be automatically evaluated and filed after being extracted from different documents in different languages or formats.
- Document AI can process the data from thousands of papers within minutes.
- With the help of document AI, users can build forms and design unique templates for business procedures, which increases productivity and saves time.
How does intelligent document processing or document AI work?
The five steps document AI system follows for intelligent document processing are:
- Document Ingest
Capturing data from diverse sources, both digital and paper-based, is the first step in document AI processing. Most document AI systems offer pre-built connectors or enable creation of unique interfaces to enterprise software for ingesting digitized data.
Intelligent document identification technology works with hardware like scanners to speed up the process of extracting data from paper-based and handwritten documents.
- Pre-processing of data
Document AI software uses structured and clean data to get good results. Intelligent document recognition software gets rid of any document-related quality issues. Different techniques including noise reduction, cropping, and binarization are used to improve the quality of data to be captured.
- Classification of data
Data points are classified into key-value pairs and line items. In semi-structured documents, position-based and context based data to be captured are also classified in this stage.
- Extraction
The data extraction process is the foundation of intelligent document processing and involves extracting data from pre-processed documents. Document AI systems extract specific data from the pre-processed and categorized material, such as dates, names, or numbers. Large volumes of subject-matter data train the machine-learning models that run IDP software.
- Post-processing and validation
In the post-processing stage, document AI systems models refine the extracted data, for instance, by fixing frequent misspellings or formatting the data based on industry standards.
The extracted data undergo automated and manual validation checks to ensure that the processed results are accurate.
Lastly, integration needs to be done. The collected data is now put together into a finished output file, which is commonly in JSON or XML format. Through APIs, the file is transferred to a business process or a data store.
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Technologies used in document processing
- Optical character recognition – Optical character recognition (OCR) is a document AI technology focuses on converting typed, printed, and handwritten text into a computer-readable format.
OCR technology allows preparation of documents in formats like PDF and JPG for additional data extraction.
While the OCR software is somewhat intelligent, it only interprets what it “sees,” not understanding the meaning of the texts which happens to be a major limitation of OCR technology.
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- Artificial intelligence – Artificial intelligence’s subject is designing, training, and deploying models that resemble human intellect. Document AI models can forecast the future and make decisions on their own after being trained on large amounts of representative data. Therefore, just like people, the models pick up on the meaning of textual data and learn to “read” imaging information.
- Robotic process automation (RPA) – Robotic process automation, which is not a component of the intelligent document processing tech stack, greatly enhances IDP. RPA bots build up on the capabilities of intelligent process automation by processing transactions, modifying the retrieved data, evoking responses, or interacting with third-party enterprise IT systems.
Wrapping up: How is document AI revolutionizing document processing?
The best document AI software integrates with multiple document workflows, business processes, and third-party platforms. The documents that can be processed using AI in document processing are:
- Invoice and bank statements
- Extraction of data from IRS forms and income and identity verification documents
- Non-standard lease agreements, sales contracts, and more
- Processing of receipts, shipping labels, and beyond
Using pre-trained APIs for bank statements, acord forms, IRS forms, and more, the document AI software improves your productivity as you don’t have to spend training the model from scratch.
An intelligent document processing software can flag missing fields, duplicate data and eliminate redundancies and error rates. Users can upload documents in bulk and the document AI software processes them for further use.