Emanuel Goldberg invented a machine that could read characters and convert them into telegraph codes. The origin of optical character recognition or OCR, the popular technology that can distinguish printed or handwritten text, began in 1914 and continues to evolve to enable document processing.
Papers are ubiquitous in many organizations as they serve to be the foundation for data and information. From applications to invoices, these documents are essential to the daily document processing workflow. Though manual documents have been playing a significant role in businesses, digitization is making inroads rather swiftly, as converting manual documents into electronic is an essential step in digital transformation. This blog explains digitization and how intelligent document processing is helping organizations in processing documents efficiently.
Gartner predicts that by 2025 70% of organizations will implement structured automation to deliver flexibility and efficiency, which is an increase from 20% of organizations in 2021.
Breaking the silos
Organizations want their teams to focus on higher-value tasks, freeing up the time and ability that are necessary to drive an improved experience for their customers and consumers. This is where the power of intelligent automation solutions helps on many levels. By tapping into the solution, they can automate many manual processes that can slow down the day-to-day crunch of meeting business objectives and goals.
Digitization Through Intelligent Document Processing
How do we convert manual forms and analog data into digital format? We are moving towards a paperless world and document processing helps us achieve it by converting manual forms and analog data into a digital format which can be integrated into everyday business processes. Solutions like AI-driven Intelligent document processing (IDP) use AI-enabled automation and machine learning (ML) to classify documents, extract the required information, and validate them. IDP is the next-gen data extraction technology, but people wrongly interchange it with OCR. It recognizes and extracts text from images or any scanned documents, converting them into machine-readable text.
IDP leverages robotic process automation (RPA) and natural language processing (NLP) tools to make the transition from analog to digital faster and error-free. There is very little or no human interference required as RPA can automate point-and-click operations.
- Processing structured, unstructured, and semi-structured documents
- Heightened data accuracy
- Enhanced security
Traditional Data Processing VS IDP
Traditional document processing uses pre-defined extraction rules to transform the relevant information into digital form. This is relevant only for structured data where the information is consistent and is not suitable for large volumes of unstructured data or complex documents where the information provided is not consistent.
Using NLP techniques, the extracted text is processed to identify and extract relevant data like names, addresses, dates, and numbers. ML algorithms are trained on a large dataset to extract specific information from invoices, forms, or contracts. This structured data is then validated which can be easily integrated into other systems for further analysis or reporting. It allows organizations to handle large volumes of unstructured data, making it an efficient solution for automating data-intensive tasks such as invoice processing, contract management, and compliance reporting.
The modern intelligent document processing solution can automate the extraction of relevant data from invoices, such as vendor information, invoice numbers, line items, and amounts. Further, it can validate the data against purchase orders or contracts, enabling efficient accounts payable processes.
Intelligent automation solutions are designed to help reduce costs and improve productivity and they can quickly identify which part of the organization requires automation, thus helping to meet revenue objectives.
Use Cases for Intelligent Document Processing
A few sectors like government agencies, financial services, insurance, human resources, etc., must still use paper-based documents and intelligent document processing will be very helpful.
Digitizing manual invoices and payroll systems is the first step toward digital transformation. With the help of intelligent document processing solutions, organizations can configure and use a pre-defined deep learning model for data extraction for the invoicing process.
It is easier for companies to convert both employee and candidate data into valuable insights that optimize staff management and hiring decisions.
Document processing has become a valuable tool to this sector as it authorizes signatures on checks and determines the authenticity of high-volume transactions, eliminating any banking discrepancies.
Using document processing, companies can extract the data from forms and quickly verify claim eligibility. Changes in the industry standards and protocols can be accommodated to safeguard sensitive documentation and personal information.
Lenders need to process millions of paper documents while processing the mortgage. Document processing here ensures faster document retrieval and filing.
HTC Global Services’ iDP solution can perform data validation using business rules or reference data to identify errors, duplicates, or missing information. It has the capability to verify and validate which involves cross-checking the extracted data against the existing databases or external sources to ensure consistency and accuracy. Advanced natural language processing techniques can be employed to enhance the accuracy of data extraction from handwritten or narrative sections.
The intelligent document processing solution is a cloud-based platform for processing all document types efficiently and effectively across industries. Organizations can quickly respond to new business opportunities without adding staff; they can power up automated workflows with valuable data, improve compliance transparency, and achieve faster ROI.
#Intelligent Document Processing
#Natural Language Processing