What is IDP?
IDP is an advanced technology that is used for automating information extraction from different types of documents, and thereafter, understanding and processing it. The fullform of IDP is Intelligent Document Processing, a system that helps in easy handling of huge volumes of documents containing lots of unstructured data, increasing overall operational efficiency and accuracy upto 99.8%. It can be used in extracting information from invoices, emails, forms, scanned documents, contracts, identification cards, purchase orders and much more. IDP works on algorithms that encompass capabilities of artificial intelligence and machine learning (ML) for handling such type of data. IDP is driven by technologies such as Optical Character Recognition (OCR), Natural Language Processing (NLP) and other ML algorithms for information extraction from unstructured data sources.
The Intelligent document processing market size was approximately valued at USD 1.1 billion globally as of 2022. It is expected to increase at a CAGR of 37.5% and is poised to reach a value of USD 5.2 billion by 2027, during this forecast period [MarketsandMarkets]. IDP software has experienced a market penetration of about 6% in the financial year 2023-2024.
The above image shows CAGR & value of IDP market in the Asia-Pacific geography from 2020-2030.
Technicalities of Automated Data Processing Systems
Components off Cognitive Document Processing
The most common components of IDP are listed below, certain algorithm-based choice may change as per task requirements and input data characteristics:
- Optical Character Recognition: This is the founding technology of all IDP solutions that performs text based extraction from uploaded, scanned or clicked images. It then converts the text present in such images and documents into machine-readable text, so as to perform text extraction as a next step.
- Natural Language Processing: It is the mechanism that helps the cognitive document processing system in understanding the meaning of the context provided in the input image or document. NLP interprets the language in which data is present, extracts the entities and cognizes the relationships incorporated between elements of the input document or image. It is powered by regular expressions and rule-based data extraction algorithms that recognize relevant patterns. Then, tokenization algorithm takes over that breaks text into words (tokens), which are identified as names, dates etc. by Named-Entity Recognition algorithm. Furthermore, Part-of-Speech tagging algorithm assigns grammar-related tags to the extracted data, and semantic analysis finally understands the text.
This analysis can be based on different types of algorithms such as Latent Semantic Analysis, Word Embeddings (GloVe, Word2Vec), Bi-directional Encoder Representations from Transformers (BERT) etc. They perform keyword matching, understand context and meaning of words and capture relationships between the phrases.
- Machine Learning: Various advanced machine learning algorithms are employed to train models in document processing automation that are used for data extraction in machine-readable format from provided documents and validation process so as to ensure utmost accuracy and reliability. Certain relevant fields are extracted along side respective key-value pairs using these models such as names, addresses, dates, amounts, document identification number and much more. Models trained on Support Vector Machine (SVM), Random Forest, Decision Trees, Neural Network (Deep Learning – DL), and Clustering (k-NN, Hierarchical) algorithms are also capable of detecting patterns and classifying (Naive Bayes) inputs as per their labelled training datasets.
Machine learning algorithms learn and adapt with new large sets of incoming unstructured data and enhance their performance towards information retrieval, identification, extraction, prediction, analysis, classification into predefined classes or types, and routing documents towards workflows, cognitive document processing pipelines as well as validation. DL architectures empower IDP through Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Transformer-based architectures (BERT, GPT) etc. for complex tasks such as automated document summarization, sentiment analysis, understanding natural language, capturing textual data dependencies and intricate patterns in the input unstructured data.
- Automation Software: Intelligent document processing solutions are often built in a manner such that they can seamlessly integrate with various softwares available at client locations that is on premise systems or over their cloud. These may include email automation, generative AI powered live chat, task management and more. Moreover, on integrating IDP solutions over indegenous workflow software companies can streamline their overall business processes through effective communication as well as routing, organizaing and storing all types of documents efficiently. If your business is primarily run using on-premise software and applications and you wish to deploy the current base or IDP solution over cloud, please do read more in our blog about cloud migration solutions.
Features of AI Document Processing
Let us take a look at some attractive features of the AI-powered end-to-end IDP platform that works on OCR engines for information extraction, Computer Vision and NLP algorithms:
- Document Localization & Input Variety: It takes all-sided inputs in multiple formats like images, invoices /receipts /purchase orders /claims /KYC documents, .pdf, .xls, .jpeg, .tiff, .png, .jpg, .html, .csv etc, and extracts fields from structured, semi-structured and unstructured data efficiently. Vision models in AI data extraction are used to localize, align and transform the input images, documents etc. for further steps in document processing automation.
- Text & Layout Detection: Accurate word detection using AI models and OCR engine to detect and recognize text accurately along with platform specific optimizations like OpenVino and TensorRT for faster inference. Deep Learning-based models are used to detect different layouts like paragraphs, titles, and parse them efficiently for AI data extraction and further processing.
- Table Parser & Text Restoration: AI models are utilized for extracting information from tables in its original structured format supporting all varieties of table structures. Various image preprocessing techniques and deep learning models are used to restore broken or faded text and shadow removal for better text recognition if required.
- Key-value & Structured Information Extraction: NLP models based accurate and intelligent data extraction of essential content from different types of segments in documents like tables, paragraphs, lines, etc. and data post processing. Automated data processing system has active learning modules for improvising on field recognition as usage multiplies. Structured information extraction is also known as Named Entity Recognition, where NLP models locate, extract information and classify entities present in unstructured text into predefined categories like receipt number, biller address, sender organization etc.
- Generative AI: GenAI-powered IDP uses Large Language Models (LLMs) for comprehensive document query and understanding. It helps in synthetic document generation using information extraction from private document repository using Retrieval Augmented Generation (RAG) based inerface. It utilizes Multi-modality Models (MMMs) for encoding and inter-modality similarity computations for generating textual summarization and visual QA application from document, images & videos, where LLMs and MMMs are also used for search and retrieval in AI document processing systems.
- Production Ready & Customizable: Dockerized for on-premise as well as API based access for cloud deployment and integration with apps like Wave, Excel, Outlook etc. Solution can be fine tuned and customized according to requirements. Output format can be in the required structured data format like .docx, .pdf, zip, .csv etc. and with high dot per images. You can enable or disable modules to extract information at different levels.
The above diagram shows evolution of Generative Pre-trained Transformers (products of GenAI)
How Document Processing Automation Benefits?
Advantages of AI Data Extraction
- Scalability: Intelligent Document Processing tools are scalable in nature that helps organizations in processing large volumes and different types of documents across their branches around the world. With due training, systems incorporated with AI document processing can process more and more documents with time and without the need of additional resources.
- Reduced Costs: Automated data processing systems help organizations in reducing their operational costs incurred from manual data entry globally. The time, cost and material savings can be utilized in more beneficial activities as a part ot strategic value-add to the business. Now on comparing the savings with respect to time, one can see clear cut profits of about 75%. For example, processing a receipt manually takes about 1-2 minutes that makes for about 50 receipts in an hour. Given that the average hourly wage in the USA is $28.34, that sums up for $0.57 as the cost of manually processing one receipt. In contrast to this, processing time by IDP systems can be as low as 18 seconds per receipt, that makes up for a minimum of 200 receipts on an hourly basis, leading to an approximate processing cost per receipt to be around $0.142. On adding the average cost of IDP software per receipt that is $0.015, one observes that the total cost comes out to be $0.157, thus saving around $0.413 or a direct improvement of 72% for processing a receipt. This value may further increase on considering the overall differences in time required to process longer receipts and the number of employees required.
- Improved Efficiency: By automating repetitive tasks that would have otherwise consumed a lot of time, IDP enhances organizational efficiency through document processing automation and effective resource allocation. Also, IDP solutions can be made platform agnostic and accessed through customized applications developed as per business needs. Please do check out our blog on custom logistics app development to know more.
- Effective Compliance: AI document processing involves stringent automated validation and regulatory compliance, while capturing and processing information that ensures accurate output and reduced errors. High degree of accuracy in processing sensitive information is necessary, especially in the case of financial and healthcare-related documents. IDP ensures of minimal or close to none occurence of errors through database cross-referencing and sometimes even involves Human-in-the-loop for this purpose, where manual verification of output is considered.
Use Cases of Intelligent Document Processing Solutions
- Invoice Processing: Intelligent document processing solutions capture, extract and process relevant information from invoices, for example, fields such as invoice amounts, currency, invoice date, vendor name, vendor address and more. This avoids long hours of manual data entry, reduces human errors and streamlines the entire accounts payable procedure. It is also used for scanning and processing credit/debit cards, for automatic invoice and bill generation that is shared with user on their retail store or banking app. Know more on how computer vision in retail is changing current scenario of brick and mortar stores on our retail-specific blog.
- Customer Onboarding: Organizations can save big time in terms of cost and hours taken for onboarding their customers across all industries, by taking up information from their submitted documents, identifications cards and forms. AI data extraction, processing, verification and output validation processes all are automated with the help of IDP systems.
- Claims Management: Banks and insurance companies make use of IDP for automating extraction of the claimant’s information from the legal claim forms accurately while adhering to industry-wise regulatory compliances within a significantly reduced processing time span.
- Contract Processing: Key-value extraction of fields and respective values such as terms in clauses of contract forms etc. is automated using cognitive document processing. This not only improves accuracy during contract review but also reduces overall processing and approval time.
- Others: IDP can be used for e-Know Your Customer document processing and verification in Government offices, hotel booking in tourism industry, cheque recognition and processing in banks, customer onboarding and payment processing in broadband and telecommunication service provider companies, inventory management and order processing in retail store chains and logistics companies, in post offices for extracting information from parcel, orders, for vendor management in IT and software companies, for reading number plates and generating automated invoices (Read more about smart parking management system in our blog) and much more.
The above images showcase claims processing using IDP & software integration.
Experience KritiKal’s GenAI-powered IDP
In this blog, we went through the transformative approach of Intelligent Document Processing, its working, components, features, benefits as well as use cases. Its future holds tremendous possibilities with respect to its current substantial benefits over traditional data entry methods in terms of flexibility, time and cost savings, scalability, improved operational efficiency, seamless integration, tangibly amplifying accuracy, easier explainability and shareability, regulatory compliance and huge unstructured data handling as it leverages various AI and ML algorithms in its functioning.
KritiKal Solutions has over 21 years of experience of working over applications of Neural Networks and Deep Learning fields and is one of the best intelligent document processing companies. We have accomplished development of state-of-the-art computer vision solutions such as our indegenous IDP solution. Our solution encompasses all features and is applicable in all use case mentioned in this blog and much more. We are currently running more than 6 different LLM models on our local GPU server with different quantization. We address all technical challenges faced during integration of IDP solutions such as inconsistent input document formats, low quality incoming images, multi-lingual documents, low output accuracy, large sets of input data, overall workflow complexity, inflexible data hosting and storage related issues, enterprise application integration and implementation, continuous model training and maintenance, ever-evolving regulatory compliances, cyber threats and data leakage, non-intuitive user interfaces and other matters, such as those faced in retail technology solutions.
As IDP technology advances, more organizations tend to embrace it to place themselves as an agile competitor in the market. Let KritiKal support you through your transition journey from manual data entry processes to IDP systems as per your specific business requirements, be it enhanced cognitive capabilities, complex AI explainability, data insights, RPA-related process streamlining etc. Please call us or mail us at sales@kritikalsolutions.com to avail our services.
Anand Mahato currently works as a Lead Engineer (Vision) at KritiKal Solutions. He has over 6 years of experience in working in the Information Technology and services industry. With his project planning, collaborative team playing efforts and strong engineering background skilled in AI/ML, Python, JavaScript, PHP, image processing, MySQL, HTML, CSS, jQuery, BigData and many more programming languages, he has helped KritiKal in successfully delivering various projects for some major clients.