To understand how to document AI works on our platform and how it is better than any other platform, we need to understand what it is and How it is implemented.
What is Document AI?
Document AI (Artificial Intelligence) refers to the use of various AI technologies, including machine learning, natural language processing (NLP), and computer vision, to analyze, understand, and extract insights from documents in various formats such as PDF, JPG, PNG, and others.
As we are moving towards a digital world with no paper trail, It is important that during this transition phase, we can confidently and accurately convert physical and handwritten data to digital data for future use and stand the test of time and nature.
With Document AI we can implement document classification, data extraction, content understanding, and document validation. For example, it can be used to automatically extract information from invoices, contracts, and other documents, and then process and organize that information for further use. It can also help businesses automate manual document processing tasks, reducing errors, and increasing efficiency. Any type of manual data entry can be automated using Doc AI more efficiently.
How is Document AI currently being used and what is the future of Document AI?
As mentioned above, Document AI has many use cases and is already being used in a wide range of industries and applications. Some of the most popular industries we deal with are as follows:
- Finance: Banks and financial institutions are using Document AI to automate the processing of loan applications, credit card applications, and other financial documents. This helps to reduce processing time, improve accuracy, and enhance the overall customer experience.
Similar processes can be built and implemented for Bank Statements, Account Creation, Bank Mandates, Bank Statements, and Insurance where we can take prompts from users in either physical format or digital to process and deliver the end product and zero to minimal human intervention with also protects the user’s data from any form of tampering or misuse. - Healthcare: Healthcare providers are using Document AI to process medical records, insurance claims, and other documents. This helps improve diagnoses’ accuracy, reduce errors, and streamline administrative tasks.
- Legal: Law firms and legal departments are using Document AI to automate the review of contracts, analyze case law, and manage legal documents. This helps to reduce the time and cost of legal work and improve the accuracy of legal outcomes.
- Government: Government agencies are using Document AI to process applications for permits, licenses, and other documents. This helps to improve the efficiency of government services and reduce the burden on citizens and businesses.
Automations like Passport verification and ID Creation can be built and deployed very easily. Similar processes can also be implemented for KYC documents too. - Education: As an industry, it has to deal with multiple types of documents which may differ from applicant to applicant. Documents may include Certificates, Results, SoP, resumes, Transcripts, and much more. With Doc AI we can consolidate all the information and even identify which applicants are best for the job automatically.
- Bills, Invoices & Receipts: These documents are a daily part of our life, anything and everything we buy some type of bill, invoice, or receipt attached to it. Having hard copies are difficult to keep track of and can become tedious if the data for those document are required at later stages. With Doc AI we can digitize these documents and use data for multiple cases like Tax evaluation and deductions, keeping track of finances, and making analytical reports, the possibilities are endless.
How does LokiBots Document AI work?
We make use of various AI technologies, including machine learning, natural language processing (NLP), and computer vision, to analyze and understand the contents of documents and implement Document AI.
The Document AI process typically involves the following steps:
- Document Acquisition: The document to be analyzed is acquired and stored in a digital format.
- Preprocessing: The document is preprocessed to remove noise, such as blank pages, borders, and other non-essential content, and then segmented into smaller units, such as pages, paragraphs, and sections.
a) Quality Check: It involves verifying the quality and integrity of scanned or digitized documents. The process of Quality Check includes checking for missing pages, broken or incomplete characters, smudges, and other errors that can impact the accuracy of document processing.
b) Classification: It involves categorizing documents based on their content and purpose. By classifying documents, organizations can automate the processing of specific document types, such as invoices, receipts, and contracts, and reduce the time and cost associated with manual document processing.
c) Re-Sizing: It is a process of changing the size and resolution of an image, typically to make it more suitable for processing. Re-Sizing can be used to reduce the file size of an image or to adjust the resolution to match the requirements of the processing system.
d) Feature Detection: It is a process of identifying and extracting specific features or patterns from a document, such as text, images, or symbols. It enables organizations to extract valuable insights and information from documents, such as names, dates, and other data.
e) Alignment Correction: It is a process of adjusting the orientation and alignment of a document to ensure that it is correctly aligned for processing.
f) De-warping: This is a process of correcting the distortion or curvature in a document caused by the scanning or digitization process. - Document AI Model: The Model consists of three main components as mentioned below and works in cohesion to produce intelligent output for human use and automation implementation.
a) Optical Character Recognition (OCR): The document is processed using OCR to convert the image-based text into machine-readable text.
b) Natural Language Processing (NLP): The text is analyzed using NLP to understand the context and meaning of the words, phrases, and sentences in the document.
c) Machine Learning (ML): ML algorithms are used to train models that can recognize and extract specific information from the document, such as names, dates, addresses, and other data.
d) OCR-Free Transfer: It is an innovative technology in the field of Document AI that has the potential to revolutionize the way we digitize and process paper-based documents. With its ability to accurately and reliably transfer information from paper to digital formats, it is set to become an essential tool for businesses and organizations seeking to improve efficiency and accuracy in their document processing workflows. - PostProcessing: This step can vary from application to application and can include multiple steps listed as follows:
a) Validation: It involves checking the accuracy and completeness of the processed document against the source. Validation is important as it helps to ensure that the information extracted from the document is accurate and complete.
b) Mapping: It is the process of assigning specific data elements from a document to corresponding fields in a database or other data storage system. Mapping is important in Document AI as it enables the automation of data extraction and storage, reducing the need for manual data entry.
c) Translation: This process of converting the text in a document from one language to another. The translation is important in Document AI as it enables organizations to process documents in multiple languages, enabling international business and cross-cultural communication.
d) Conversion: This process of converting a document from one file format to another. Conversion is important in Document AI as it enables documents to be processed in a format that is suitable for the intended use.
e) Transformation: This process of changing the structure or format of a document to make it more suitable for processing or analysis. Transformation is important in Document AI as it enables organizations to extract more valuable insights and information from documents, such as relationships between data elements or patterns in the document content. - Human In Loop: This is an optional step and can be implemented as a user requirement where they need a human to verify the document output and alter them if required.
- Output: The final output is generated, which can be in various formats, including structured data, summaries, and visualizations.
Overall, we use a combination of OCR, NLP, and ML techniques to analyze, understand, and extract valuable insights from documents. The output can then be used for various applications, such as automating manual document processing tasks, improving decision-making, and enhancing productivity.
LokiBots Doc AI and Advantages
Document AI is one of the main pillars of our platform. Our platform allows us to read the data and cognitively analyze the data to provide intellectually sound answers.
Unlike other platforms which only make use of Optical Character Recognition, we also apply Machine Learning to it to provide answers to various questions one might have for a document. For example, let us consider a resume, it may consist of multiple sections, tables, bullet points, links, and much more. Once we pass It through Doc AI, It will parse through the document and make links between different types of information present in the document. So if we would like to know how many years of experience does this person have? The model will add up all the years of experience and provide you with the answer.
Being a cloud-native platform that offers a subscription-based pricing model with pay-as-you-go, LokiBots can help customers to automate their business processes with zero Capital Expenditure and minimal Operational cost. LokiBots provides a feature where a process can be automated in smaller chunks and later combined. Dividing the entire process into smaller chunks can be integrated and automated with our RPA bots which will help a user in many ways. First, if subprocesses are independent of each other, the subprocesses can be automated in parallel and tested individually. Second, if there are some changes to be done, these changes can be easily done without affecting the other subprocesses, making it easy to manage the process.
Conversational Automation
Along with Document AI, we provide Intelligent Automation and Conversational AI, which can all be combined to provide a very intelligent human-like solution and interactivity to many problems.
Let’s consider you are a large company and millions of people from around the world apply for different positions open at your company. Going through each application and various supporting documents provided by the applicants can be very resource heavy and costly.
By combining the three pillars of our platform we can provide an end-to-end solution where Intelligent automation will gather all the data and relations from the company portal, emails, or any other source of data. Then feed it to our Doc AI model to process the data and extract relevant information and save it in an appropriate format. We can then pass the data through Conversation AI, which can then answer general questions like “I am looking for top 10 candidates with 3 years experience in Java & Python and located in Bangalore?” and the AI will provide us with relevant information.
This can reduce months of work to days and save a lot of resources for the company. Implementations like these are highly dynamic and can customize as per customer requirements and unique situations.
Future Prospect of Document AI
The future of Document AI is bright, as advancements in AI and machine learning continue to drive innovation and improve the accuracy and efficiency of document processing tasks. Some potential future applications of Document AI include:
- Enhanced automation: Document AI is expected to become more automated and intelligent, allowing organizations to automate more complex document processing tasks.
- Improved accuracy: Advances in AI and machine learning are expected to improve the accuracy of Document AI, reducing the need for manual review and validation.
- Increased integration: Document AI is expected to become more integrated with other business applications, such as customer relationship management (CRM) systems and enterprise resource planning (ERP) systems.
- More personalized experiences: Document AI is expected to enable more personalized experiences for customers, allowing organizations to tailor their services and products to individual needs.
Overall, Document AI is expected to play an increasingly important role in helping organizations automate document processing tasks, improve efficiency, and drive innovation.
Want to know more about LokiBots, and are interested to start your DocAI journey?
Reach out to us at Sales@LokiBots.com.
For more details, visit https://www.lokibots.ai/
About the Author
Sahil is working as an Automation and Machine learning engineer in LokiBots Inc. He is currently working on various automation use cases across various domains. He is currently involved in designing process flows, implementing them by leveraging AI/ML models, image processing and various other tools required for the process implementation.
