Document processing involves the conversion of manual and analog forms of information to the digitized format to integrate them into the daily business processes. Every company considers manual data to electronic document conversion as the core step in their digital transformation journey. A document-processing system can help organizations digitally replicate the original structure, images, and layout of a document. Intelligent Document Processing (IDP) solutions aid businesses in the data extraction from unstructured and complex documents. IDP manages the document variation and complexity through the automation of document processing with the use of Machine Learning and various AI technologies.
This blog discusses document processing in detail and how its automation can help companies to deal with the complexity of managing massive volumes of business documents.
So, let's jump right into it:-
Document Processing is defined as the procedure involved in the conversion of physical documents and the related forms into digitized form with the data extraction, thereby arranging it to a relevant structured format.
Documents are of various formats, file types, and include value-specific information. In the old manual document processing approach, you are manually involved in document processing, which is prone to error, time-consuming, and expensive. Nevertheless, you require the information to be used within the documents to enhance the downstream application in a short period, which is impossible with the cumbersome manual document processing.
This is why an automated document processing solution is preferred over the mundane manual document processing. Automation of document processing, in line with the advanced technologies, can give you the benefits of core process integration, elimination of manual labor, meeting the compliance requirements, and solving the issues faced in processing the complex documents.
Document processing involves different methods such as neural networks, computer vision algorithms, and manual labor. The process of analog to digital data conversion involves the below steps that make document processing effective.
Document processing solutions are based on a specific set of rules. Once these predefined rules are defined, the team implements structure and layout extraction.
OCR or Optical Character Recognition involves the scanning of documents from the manual documents and their transformation to data. HTR or Handwritten Text Recognition is another intelligent character recognition technology used to recognize the standard text as well as different styles and fonts of your handwriting.
The OCR technology is sensitive to errors, hence requiring a manual review post the data extraction. If a document cannot be processed or errors are recognized, it can be further flagged for a human review, and you can fix the errors with a manual entry.
The processed document is stored in an ideal format that lets it integrate with the existing business applications.
The process involved in the transformation of unstructured or semi-structured information into the relevant data is called Intelligent Document Processing.
About 80% of the business data is integrated into unstructured data formats like emails, business documents, PDF documents, and images. Since business data is an integral part of digital transformation, IDP- the next generation document processing solutions can be used to extract and process data from various document types.
It utilizes the Artificial Intelligence along with Natural Language Processing (NLP), Deep Learning, Computer Vision, and Machine Learning, to segregate, classify, and extract, the required information and evaluate the extracted data.
The advancement in Artificial Intelligence (AI) has encouraged companies to implement document processing automation. IDP utilizes AI-driven automation and Machine learning techniques to classify documents, extract data, and perform data validation. It can further automate and accelerate document processing through data structuring and automation.
It can also incorporate the Natural Language Processing (NLP) and Robotic Process Automation (RPA) tools to ease the analog-to-digital transition and be least error-prone. RPA can enable the automation of operations to minimize human involvement in the entire process.
Intelligent Document Processing (IDP) platforms incorporate the following components to transform the documents into appropriately labeled digital data. The IDP platforms should be:-
IDP can be used to automate the data extraction from unstructured and complex documents to drive the business processes. The extracted data can be utilized by businesses to proceed with their business automation and the automation of document classification.
It is difficult to implement accurate auto-data extraction from complex and rigid documents since they do not rightly fit into the pre-defined templates. Without IDP, the companies will end up in the manual classification and extraction of data from the documents. IDP is an affordable, flexible, and fast alternative to complicated manual document processing solutions.
The major difference between the traditional document capture and IDP is the feature of innovation in the way the documents get processed. The popular names in the traditional types of document processing stopped their advanced solutions years ago.
This is because of two core reasons:
Firstly, these tools were developed in the era where the software architecture was not built with adherence to scalability. With time, the data-driven applications accelerated the demand for scalability, which is missing in the earlier tools.
The other reason is that the traditional document capture companies have a huge customer base. Such businesses find it profitable in the way they work now and would not wish to disrupt the workflows of existing customers with the upgrades.
They follow the development of Robotic Process Automation and other technologies to amplify the IDP technologies instead of bringing the best innovations, which is the bitter truth.
The other techniques involve the image processing triggered by the computer vision that helps document with Optical character Recognition and archival. An IDP platform facilitates the creation of two versions of the digital documents, one optimized for on-screen viewing in the CMS (content management system), and the other for machine-reading.
IDP incorporates four core functions:
IDP is a powerful tool that eradicates or minimizes human intervention for data extraction and manual data processing.
It can classify documents automatically into various categories depending upon the content and structure. The advanced document processing solutions can accept numerous documents, and automatically classify them to be routed to the appropriate work queues. They accelerate document processing and eradicate the manual effort that might be cumbersome for smart automation.
IDP evaluates the extracted data adhering to the set of business rules, document comparisons, and various other sources. It is crucial to analyze the extracted data to make sure it is accurate. The validated data is further sent to processing, and the data which fails in the validation is sent for correction.
Firms use IDP to identify the data they extract to gather insights, make actions and drive the business decisions with insights. Ensure that you enquire with the IDP vendor about the particular functionality they provide since it can vary.
IDP processes the documents with image and text complexity. The complexity of text includes mixed fonts, footnotes, text with images, multiple documents in one PDF, long documents, etc. The image complexity consists of graphs, tables, mixed meaning, complex structures, unusual elements, or noisy images.
Additionally, IDP processes unstructured documents that have changed format and location over time. For instance, these are the documents where the same data point is found in various locations, based on the type of the document, version, and source.
Here are some of the most common situations where IDP could be the perfect fit you can utilize:
The manual invoicing system and payroll processing system need digitization and automation. When you use Intelligent Document Processing, you can enable the configuration and utilize deep learning models to implement data extraction.
With document processing, you can extract the data from various forms and assess the eligibility/coverage. It can also keep the documents consistent in line with the industry-relevant protocols and standards. Additionally, IDP can protect personal information and sensitive documentation.
Automated Document processing can convert the employee days to the relevant insights to optimize hiring decisions and staffing management.
The tool can effectively aid the financial services, authorize the signature on financial checks, analyze the authenticity of transactions with high volume, etc. to check against discrepancies in banking.
The lenders process numerous paper documents every year. Intelligent Document Processing paved the way for instant and simple document retrieval and improves the speed of the mortgage filing process.
If you use IDP, it gives you endless benefits compared to the traditional document processing system as follows.
With advanced automation, you grab a faster way to extract accurate information from unstructured data. It can minimize the workflows by eradicating the manual operations involved.
IDP can be useful to implement the transformation of unstructured, structured, or semi-structured documents to enhance business workflows.
Machine learning can enhance information extraction, document classification, and data validation to trigger the reliability and the quality of processing. The accuracy of the workflows can be improved with low-code supervised training.
IDP can be used to store personal information and documents in a digital location. This security is very important in the industries such as financial and healthcare segments with stringent regulations and compliance standards.
Manual document processing is time-consuming and makes the process hectic. Automation can cut short the time minimizes operational expenses and enhances staff utilization.
The substantial factors that manage IDP are disruption, innovation, and evolution. The modern business ecosystem can thrive only with a catalyst to expedite its workflows.
In every firm, data is the primary tool that plays a meaningful role in the transformation journey. By gaining new data sources, and by finding novel analysis methods, companies gather valuable insights required to cultivate digital transformation.
Data is the crucial element to 'go digital'. The digital transformation has created new products, capabilities, operating models, and value propositions to attain disruptive success for every organization.
Docsumo helps industries with simplified data extraction, bank statement and invoice processing, automated data extraction, lease agreements data processing, landing and shipping labels/ receipts processing, and so on.
The major advantage of utilizing Docsumo document processing solution is the use of pre-trained APIs for certain document types like acord forms, bank statements, invoices, licenses, IRS forms, and so on. Hence, you don't have to spend more time training the models from the beginning.
Docsumo APIs consider duplicate data entries, missing values/fields, to eliminate the redundant rates. Once the data extraction by the APIs is complete, users shall review and approve the changes using the platform. Finally, users can also upload the documents in one go and implement the processing for later use.
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.