As companies grow in size and operations, managing physical documents becomes increasingly inefficient and costly. Therefore, transitioning from traditional to digital documents is a better option for any organization dealing with vast quantities of paper documents. Choosing the right technology can make scanning and storing this data unbelievably easy; accessing digital copies is also quicker than retrieving physical documents. Additionally, scanned documents:
- Make it easy to locate files and manage data
- Shorten the overall turnaround time
- Reduce the overheads of physical storage
- Improve data sharing and collaboration
- Enhance customer service
- Increase data security
- Support IT and regulatory compliance
- Support disaster recovery
In today’s digital world, the dependence on scanned documents has become increasingly prevalent across most industries. Banks can benefit from scanning cheques and customer documents from their respective branches, increase their operational efficiency, and significantly reduce administrative costs. Large educational institutions can access data on their teachers, support staff, and thousands of students, within a matter of seconds.
Introduction
Scanned documents serve as the digital store of essential information including legal contracts, financial statements, invoices, reports, bills, product descriptions, etc. These documents contain valuable information, which needs to be properly preserved, easily accessed, and efficiently leveraged. Despite the convenience of their digital format, the time and resources required to manually go through and pull-out data from these scanned documents can reduce a team’s productivity. It can also lead to inadvertent errors during manual data entry and cause other delays, which in turn can have unintended consequences on the operations and profitability of a company.
The inherent irregularities of scanned documents present several obstacles to their easy consumption and prevent companies from harnessing the full potential of their data. The typical challenges issues include variability in quality, formatting inconsistencies, and the presence of unstructured data, notably within tables.
Intelligent Document Processing (IDP) of scanned documents, involves the use of advanced technologies such as Optical Character Recognition (OCR), Natural Language Processing (NLP) and machine learning algorithms to accurately identify, extract, interpret, and organize data from scanned documents.
A report by Forrester Research, a leading global research and advisory firm, found that businesses, which implemented Artificial Intelligence (AI) powered document processing systems saw an average ROI of 303%. Similarly, a report by the International Data Corporation (IDC) found that businesses, which have implemented AI-powered document processing systems have seen an average increase in efficiency of 42%. While specific numbers may vary based on the nature of the business, industry, and the extent of automation implemented, Forrester has indicated that organizations can achieve an average ROI of 150% or more within the first year of adopting intelligent document processing solutions.
Six Stages of Intelligent Document Processing (IDP)
- Document Classification
This consists of assigning different categories to the documents as per the overall requirement, to help store and analyze them. These categories can include the file format, quality, and domain type. Correctly classifying the scanned documents leads to the creation of suitable solution workflows.
- Document Pre-Processing
Pre-processing consists of improving the text alignment, finding features of interest, and suppressing unwanted information which is noise. The overarching goal of this stage is to prepare the image as best as possible for the data extraction.
- Data Extraction
Using contours and kernels, the solution will enhance the scanned image and find the coordinates of objects that need to be extracted. OpenCV and Pytesseract libraries of Python are commonly used for this.
- Data Validation
This can be carried out using 2 different approaches. In the first approach, a randomly selected test set is created, and the trained object detection model is evaluated on the same. The second approach involves testing unseen documents and improving upon the overall performance by fine-tuning the model.
- Data Transformation
The transformation of extracted data is required to save and publish the data which is required for downstream tasks. For example, the transformation of end of day balance from a bank statement is needed to analyze the overall debit and credit transactions of a given customer.
- Data Integration
The last step is to connect the workflow with other business applications and tie the results to the overall value chain.
Benefits Of IDP
- Improved Efficiency
By automating manual document processing tasks, IDP reduces the overall time and effort required to handle large volumes of scanned documents.
- Higher Accuracy
IDP solutions can capture data with higher accuracy than humans. It also helps to identify and eliminate process inaccuracies by verifying the extracted data.
- Improved Security & Compliance
Companies use IDP to ensure compliance with regulatory requirements by accurately capturing and processing critical information from documents.
- Reduced Costs
Automation of document processing tasks can lead to significant cost savings by reducing the need for manual intervention and helping streamline document-centric workflows.
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Chandra Prakash is a postgraduate in Engineering. He is interested in machine learning, mathematics, and programming, and is curious about history and science fiction movies.
