What is AI Document Processing? Explained
Processing documents manually has long been resource-intensive and inefficient for many organizations.
Businesses spend over $120 billion annually on non-digital document-related activities like data entry, handling physical files, and other administrative tasks. As volumes of documents continue rising each year due to growing digital transactions, these costs are becoming unsustainable for most.
To address these challenges, businesses urgently needed technology that could intelligently automate document workflows at scale. This gave rise to AI Document Processing solutions, which apply advanced artificial intelligence and machine learning techniques to not just digitize documents but comprehensively understand, extract, and analyze information automatically with human-level precision.
Initial customers that embraced these solutions are already gaining significant competitive advantages through higher output, greater accuracy, and massive overhead reductions.
What is AI Document Processing?
AI Document Processing refers to applying advanced artificial intelligence and machine learning techniques to automate the intake, extraction, analysis, and processing of structured and unstructured documents.
Traditionally, organizations had to rely on manual data entry or document scanning software or tools to extract and process information from documents. However, these legacy approaches are limited in their capabilities. They cannot comprehend context, require templates for each document type, and often result in low accuracy.
AI document processing leverages techniques like:
- Optical Character Recognition (OCR) to recognize text in images/scans
- Natural Language Processing (NLP) to comprehend language semantics
- Computer Vision for visual data recognition
- Machine Learning models that learn continuously from user feedback
Combined with features like information extraction, validation, classification, and automation – AI document processing aims to achieve the following:
- Digitize paper and digital documents entirely into structured, indexed data
- Extract all relevant information like names, amounts, and dates from documents accurately
- Organize extracted data into organized, validated databases
- Automate repetitive document workflows end-to-end
Therefore, it applies human-level comprehension to documents using AI technologies in order to undertake knowledge-intensive document tasks at scale for businesses in a highly efficient manner.
How Does AI Document Processing Work?
At a high level, a typical AI document processing workflow involves the following key steps:
1. Document Ingestion
The first step is getting documents into the AI document processing platform. This can be done by uploading files directly via APIs, cloud storage integrations, or automated uploads from document repositories, scanning software, etc.
The documents can be in any standard format like PDF, DOC, JPG, PNG, XLS, etc.
2. Document Pre-processing
Once ingested, documents go through various pre-processing steps:
- Optical Character Recognition (OCR) converts images and scans into machine-readable text
- Page splitting separates multi-page files into individual pages
- De-skewing and despeckling fixes issues in scanned documents
- Color normalization standardizes colors for better processing
3. Document Classification
The AI model classifies documents into appropriate categories/types based on visual and textual clues. This could include invoices, receipts, contracts, etc.
4. Data Extraction
Relevant data is extracted from the documents using techniques like:
- Named entity recognition to identify key items
- Field-level extraction via templates or free-form reading
- Table and field data extraction through computer vision
- Converting extracted values into structured JSON/XML formats
5. Validation and Enrichment
Extracted data undergoes validation checks against predefined rules to ensure completeness and integrity. Missing or ambiguous values can also be enriched at this stage.
6. Post-processing
This involves attaching extracted data to original documents, converting files, etc. It also includes features like flagging exceptions, similarity detection, and assigning manual review tasks.
7. Automation and Integration
The structured, machine-readable output is then integrated with various backend systems for further automation, like ERPs, CRMs, BFSI apps, and analytics dashboards using REST APIs. This fuels various document-centric processes and actions.
With ongoing training on new document types and user feedback, the AI models continue to get smarter, reducing reliance on manual labor or rigid templates over time. This enables highly scalable and efficient document processing.
Best AI Document Processing Tools
There are many AI document processing tools available today that implement the above workflow to automate document-centric processes for enterprises. Here are brief descriptions of some top players in this space:
1. KlearStack
Klear Stack offers a comprehensive IDP solution that leverages technologies like OCR, NLP, AI, and ML. It can extract, classify, and process documents at scale.
Some key capabilities include automated capture from multiple sources, intelligent classification using AI models, and efficient processing through advanced algorithms. It supports various data types and has applications in areas like accounts payable, supply chain, consumer loans, etc.
Klear Stack claims up to 80% cost reduction, 500% efficiency gain and 99% accuracy. It provides template-less processing through self-learning AI models. The models can be trained on customers’ datasets and continuously improve using user feedback. Klear Stack delivers an intuitive UI that allows users to get started quickly.
Under the hood, Klear Stack utilizes deep learning and computer vision techniques to understand both text and visual elements in documents. The platform has pre-trained models for common document types and enables custom training.
Klear Stack integrates seamlessly with various internal systems and downstream applications via open APIs. It ensures the security of customer data through encryption and access controls. Customers can choose data hosting regions as per their requirements.
Klear Stack delivers continuous innovation through regular product updates. It aims to help organizations achieve the highest automation and data accuracy levels through an advanced document processing platform.
2. Docsumo
Docsumo is an AI-powered document processing platform that allows pre-processing scanned or digital documents, classifying them, and extracting structured data using intelligent models. It comes with both pre-trained and customizable models.
Using OCR and computer vision techniques, the platform supports data extraction from various document types, including invoices, statements, and bills. Docsumo automatically classifies documents and routes them through defined workflows.
It leverages deep learning algorithms to validate data against business rules and ensure touchless processing. Users get real-time insights into automation performance using interactive dashboards.
Docsumo makes model training intuitive for non-technical users through an easy-to-use interface. It ensures security, privacy, and control over customer data.
Customers can integrate extracted data into various applications and databases using Docsumo’s APIs. This facilitates processes like order management, finance automation, etc.
Docsumo aims to minimize manual work for organizations through high levels of straight-through processing. It delivers a scalable, cost-effective, and accurate solution for processing large volumes of documents.
3. Rossum
Rossum offers an AI-first platform called Rossum Aurora, specifically designed for transactional document automation. The platform leverages cutting-edge technologies like LLMs for downstream processes.
Some key capabilities include document intake using OCR and parsing, structured data extraction using LLMs trained over millions of records, and integrated validation workflows.
Rossum Aurora can support various complex transactional document types, including invoices, orders, freight documents, etc. It comes with pre-built templates for common processes.
The platform is highly customizable, allowing users to configure solutions fitting their unique needs through a low-code interface. It automates workflows end-to-end, from intake to approvals.
Rossum drives high rates of straight-through processing using features like auto-classification, exception handling, analytical dashboards, and reporting. It can scale to process millions of records annually.
The platform continuously improves through ongoing training on real customer data and user feedback. It delivers configurable automation that integrates both human and AI abilities seamlessly.
Enterprises across industries have deployed Rossum to transform core transactional operations, achieving up to 10X increase in processing efficiencies.
Benefits of AI Document Processing
AI document processing automates away labor-intensive paperwork, bringing transformative impacts to businesses:
1. Increased Efficiency
Automating repetitive manual document tasks through AI yields drastic time savings. Reductions in document processing efforts as workloads shift to AI continuously. Freed capacity propels new strategic initiatives.
2. Lower Operational Costs
By offloading document processing workstreams to AI, organizations cut overhead spending on temporary/outsourced resources for data entry. Payback arrives within 6 months typically.
3. Enhanced Accuracy
AI systems matched extraction accuracy of 95-99% far outstrips error-prone human rates 5-10%, avoiding costly consequences and rework from mistakes.
4. Scalability
AI easily expands capabilities to absorb exponentially growing volumes without adding staff, making automation highly suitable for industries processing millions of records annually.
5. Actionable Insights
AI extracts deep insights from documents formerly requiring manual review or remaining unexplored due to resource constraints.
6. Regulatory Compliance
Consistent, automated processes implemented through AI reduce risks of non-compliance associated with manual mistakes or delays in regulated areas like healthcare and finance.
7. Enhanced Customer Services
Streamlined document processing via AI improves turnaround times, decreases errors, and optimizes experiences for customers, vendors, and other external partners.
Conclusion
AI document processing has revolutionized how organizations tackle increasing document volumes and related costs that were long deemed inevitable expenses. Through automation powered by self-learning systems, leading entities across industries are achieving up to 10X gains in productivity while mitigating risks.
As the market matures, AI is poised to transform previously labor-intensive back-office operations concentrated on documents into high-value, insights-driven functions. While challenges around model governance, explainability, and oversight persist – AI is reengineering business processes at a tremendous pace optimized for intelligent automation.
