Automated document processing combines AI with deep learning or artificial intelligence to eliminate manual document processing and classifies and extracts information from business documents quickly, easily, and accurately.
In this article, we discuss how automated document processing works, and what are its key components.
So, let’s jump right into it:-
Automated document processing techniques
The process of converting manual and analog forms of information into digitized format involves different automated document processing technologies such as:
#1. Computer vision
Computer vision is a technique that enables machines to "see" by mimicking human eyesight. While first appearing futuristic, computer vision is a natural extension of artificial intelligence (AI) and deep learning swiftly taking over all different industry types.
We can recognize objects and visual patterns quicker with computer vision than with the naked eye. Computer vision also lessens employee fatigue by reducing rote, repetitive activities from being performed by staff members.
Computer vision-based document processing automation recognizes patterns. Here’s how computer vision-powered document processing helps:
- Automates tasks that would otherwise require human supervision using pictures detected by a camera
- Creates algorithms to recognize patterns from documents
- Recognizes and captures information from images
- Identifies patterns more quickly and accurately than a manual labor
#2. Zonal OCR
The second generation of optical character recognition (OCR) technology is called zonal or template-based OCR. It is used when certain portions of a document need to be extracted preferentially or "zonally."
In other words, regular OCR extracts all data from documents and converts it into digital format with no differentiation based on relevance. This means, the data requires further manual processing to extract relevant information from the original document.
Whereas zonal OCR extracts specific fields from the scanned documents such as tables and columns, and stores them in a structured format for further processing.
Here’s how zonal OCR works:
- It can identify the structure of a document through APIs
- OCR software then splits into zones corresponding to specific fields
- The zones are extracted as specified in the template
- Zonal OCR can be trained to ignore graphic elements that can be ignored to reduce the amount of information that needs to be parsed to extract specific data
#3. Intelligent document processing (IDP)
Intelligent document processing is defined as automated data capture from multiple documents and data sources and organizing it for further processing. IDP deals with the complexities of processing huge volumes of data accurately and within seconds.
The typical IDP workflow involves:
- Using computer vision algorithms to recognize document layouts from scanned images and files in both paper-based and digital formats.
- NLP technology recognizes characters, symbols, and numbers from tables, paragraphs, and unstructured text in documents.
- Using OCR, entity recognition, sentiment analysis, and feature-based tagging, it reads information and inputs it into data management or content management systems with more than 99% accuracy.
What are the core components of intelligent document processing?
An IDP platform should be:
- Flexible to extract, ingest, and validate data from unstructured, semi-structured, and structured documents
- Industry agnostic
- Scalable so that users do not have to train the API in case of minor changes in the document and be able to process a large number of documents
- Capable of processing bulk documents through batch processing
- Secure and end-to-end encrypted to prevent data leaks and privacy breaches
- Offer third-party integrations across on-premise and cloud content management systems
- Cloud-based so that it can be accessed from anywhere and across devices
How does IDP work?
Intelligent document processing software uses machine learning to extract data from documents. There are five steps in the intelligent document processing workflow.
Step 1: Document pre-processing and ingestion
The first step in IDP is capturing data from multiple content types and preparing it for processing. Preparation involves merging or splitting of documents, data validation, and correction. Some tools also allow data labeling and annotation for improved accuracy with human-in-the-loop.
Step 2: Data classification
Data is classified into different categories based on content and structure. Advanced solutions possess the capability to accept documents at scale and classify them to be routed to appropriate work queues. Alternatively, the software offers suggestions for categories based on existing taxonomies. At this stage, humans can create categories and data validation.
Step 3: Data extraction
Machine learning allows the document processing software to extract data from various content types and allows the handling of diverse formats. Advanced intelligent document processing software like Docsumo require less training than other ML models to quickly and accurately extract data. Humans can train ML models and APIs to identify fields for extraction.
Step 4: Data validation and feedback
IDP validates extracted data against business rules, document comparisons, and internal/external data to make sure that it is accurate. From here, the validated data goes for further processing whereas the data that fails validation is sent for correction.
Step 5: Integrations, business intelligence, and insights
Firstly, the validated data is sent to third-party software and downstream applications for use. Data enrichment tools, customer service platforms, and RPA solutions are common IDP integrations.
This data is then used for gathering insights, decision-making, and business process improvements. Ensure that the IDP software generates insights and can integrate with business sources for data to flow between different applications without manual intervention.
Some reasons for the widespread adoption of intelligent document processing solutions include:
- Its ability to process documents with text and image complexity.
- Text complexity - Footnotes, mixed font, text with images, long documents, and multiple documents within a PDF.
- Image complexity - Graphs, tables, noisy images, complex structures, and unusual elements.
- IDP can also process unstructured documents where the location and format have changed over time. For example, documents with the same data points are found in multiple locations with changed version, format, and source.
Benefits of IDP over other document processing technologies
Firstly, the benefits of implementing IDP span across businesses of all sizes and industries. In a nutshell, the most important advantage of automated document processing includes avoiding errors commonly associated with manual data extraction. To know more, keep reading.
#1. Boosts document processing efficiency by 10X
Intelligent document processing software processes, ingests, validates, and classifies data within 30-60 seconds. Using automation, it can process structured, semi-structured, and unstructured information across multiple formats without human interaction. Human-in-the-loop can be used to review exceptions.
Unlike humans, the software can process documents at scale without being subjected to fatigue, thereby improving efficiency by 10X.
#2. More than 99% accuracy in data extraction
Advanced software eliminates data entry errors and enables the output to be accurate to more than 99%. What’s more, automated document processing is scalable with the same efficiency 24/7.
With improved accuracy, the same quality of output is maintained throughout the data extraction process leading to a reduction in the wastage of time and resources for data aggregation and validation.
Modern intelligent document processing software have added functionalities of flagging errors, data verification, and more that play a major role in ensuring streamlined document processing.
#3. Improves STP (Straight-through-processing) up to 95%
Docsumo improves the STP with completely automated document processing by eliminating human intervention, the cost of manual processes and errors.
In absence of STP, a business has to go through multiple touchpoints to process data from unstructured documents and manually enter them into the accounting system or ERP.
With STP, using OCR or intelligent document processing, all transactions are made within seconds and error-free.
#4. Operational costs reduce by 65-70%
Implementing IDP software may have an upfront cost as well as a learning curve for employees. But the long-term ROI gains are unmatched.
Here’s how IDP reduces operational costs:
- The software saves monetary resources by preventing duplicate entries likely to happen owing to human errors. It can be calibrated to send automated alerts in case of duplicate entries.
- Storing data digitally reduces the printing cost and this information can be archived over the cloud.
- Cloud-based Docsumo has the capability of flagging fraudulent payments.
#5. Reduces data processing time to 30-60 seconds
All data extracted from documents and file types get stored over a centralized repository in the IDP. Different teams such as accounting, payments, and so on can access and pull this data within seconds. The single source of truth stores purchase orders, goods received notes, lease agreements, and any documents from which data is extracted.
Unlike the manual processes that could take weeks or days, the IDP extracts, classifies, ingests, or sends the data to third-party software within 30-60 seconds.
IDP also enhances data for RPA/AI consumption by turning streams of unstructured data present in documents into streams of cleansed, structured data. It saves resources on data input, makes essential information available faster, and allows enterprises to establish a digital workforce.
In addition, the IDP improves interdepartmental communication as the required documents are available over the cloud, freeing up the employees’ time to focus on value-driven activities.
In a nutshell, the overall efficiency of your operations increases significantly.
#6. Enterprise-grade security
At a time when enterprises are adding regulatory safeguards to their compliance, fine-grade security offered by intelligent document processing software like Docsumo ensures that the critical data is secured. Features like audit trail, version control, and data encryption along with GDPR and SOC–2 compliance are some of the measures to provide a secure experience to users.
#7. Easy integration with the existing tech stack
Another benefit of intelligent document processing systems is their ease of integration with current software and hardware which allows for the flow of data from different sources, saving time on data consolidation.
Automated document processing paves the way for improved operational efficiency and profitability
For businesses that still rely on paper-based workflows, document processing automation is an effective solution to eliminate inefficiencies, reduce workload and enable smooth document processing. AI-based IDP tools for automated document processing like Docsumo streamline document processing for improved operational efficiency and profitability.
Docsumo for document processing automation
Docsumo’s intelligent document processing automation software enables businesses to extract data easily and efficiently from both structured and unstructured documents. The self-serve interface comes with pre-trained models for most common business documents so that you can get started immediately.
What makes Docsumo the best document processing software are the following features:
- NLP-based data categorization
- Auto-classification of documents
- Real-time validation, verification, and approval of data from the database
- Ingest documents from any channels
- Wide range of use cases across industries such as banking, insurance, lending, and logistics
If you’re planning to implement an IDP solution to automate your business’s document processing, sign up for a free trial.