Do you spend hours each day manually processing documents? Or you find that your work is stalled till your data processing team reviews the data extracted by OCR? If so, you’re not alone.
Businesses of all sizes struggle with the time-consuming and error-prone task of manually extracting and reviewing data from documents. But there’s a better way.
IDP is like having a virtual assistant that handles your document-related tasks automatically. Powered by advanced AI technology, IDP automates data extraction, content analysis, and document organization, making your work life easier.
At its core, IDP aims to eliminate the manual effort, errors, and inefficiencies associated with traditional document processing methods. By utilizing advanced OCR (Optical Character Recognition), NLP (Natural Language Processing), and ML techniques, IDP can understand the structure and content of documents, extract relevant data, and automate subsequent actions based on predefined rules.
There are many benefits to using IDP, including:
At its core, IDP combines the power of AI, ML, NLP to automate document processing workflows. Let's break down the technology stack that drives its remarkable capabilities:
OCR forms the foundation of IDP. It scans and analyzes documents, recognizing and converting printed or handwritten text into machine-readable data. This enables IDP to extract information from various document formats accurately.
IDP utilizes ML algorithms to train its models. These models learn from vast amounts of data to recognize patterns, identify document structures, and improve the accuracy of data extraction over time. ML enables IDP to handle diverse document types and adapt to varying layouts.
IDP's NLP component adds linguistic intelligence to the system. It enables IDP to comprehend and interpret human language within documents, extracting meaningful insights, identifying entities, and understanding contextual relationships. NLP enables IDP to process complex language structures and extract specific information accurately.
A data extraction rules engine allows users to define and customize extraction rules based on their specific document requirements. This engine enables IDP to adapt to different document formats, layouts, and variations, ensuring precise extraction of desired data fields.
ANN is a machine learning model inspired by the structure and functionality of the human brain. It can be utilized in IDP to enhance the accuracy of data extraction and improve the system's ability to learn and recognize complex patterns in documents.
IDP can incorporate data analytics and reporting capabilities to provide deeper insights into document processing workflows. By leveraging data visualization tools and analytics algorithms, IDP can generate reports, identify bottlenecks, measure key performance indicators (KPIs), and drive continuous process improvement.
This technology enables IDP to interpret and understand visual elements within documents. It can detect and extract information from images, such as logos, signatures, stamps, or specific regions of interest. Computer vision algorithms help improve accuracy in scenarios where visual content plays a crucial role.
IDP can integrate with other technologies, systems, and platforms using application programming interfaces (APIs). This allows seamless data exchange, synchronization, and collaboration between IDP and other enterprise systems like CRM, ERP, or document management solutions, maximizing the value of document processing automation.
It’s important to note that the specific technologies incorporated in an IDP solution may vary based on the provider, the specific use case, and the desired functionalities. The core technologies of OCR, ML algorithms, and NLP form the foundation of IDP, while additional technologies can be integrated to enhance its capabilities and tailor it to specific requirements.
IDP begins by ingesting documents from various sources, such as emails, file uploads, or integrated systems. It supports multiple file formats, including PDFs, scanned images, Word documents, and more.
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The system performs pre-processing tasks, such as image enhancement, noise reduction, and skew correction, to optimize the quality of document images. This ensures accurate data extraction in subsequent steps.
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IDP uses ML algorithms to classify documents based on their content and purpose. It automatically categorizes them into predefined classes or custom categories, making it easier to organize and route documents accordingly.
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IDP employs OCR, ML, and NLP techniques to extract specific data fields from documents. It can capture customer names, addresses, invoice numbers, purchase details, or any other desired information, with high accuracy. Advanced techniques like entity recognition and pattern matching help identify and extract complex data structures.
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IDP validates and verifies extracted data against predefined rules or databases. It ensures data integrity and accuracy by performing checks for consistency, completeness, and compliance. Any discrepancies or errors can be flagged for manual review or automated resolution.
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IDP seamlessly integrates with existing systems, such as Content Management Systems (CMS), Enterprise Resource Planning (ERP) software, or other business applications. It automates document routing, triggers actions based on extracted data, and initiates approval workflows, enhancing efficiency and collaboration.
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Our IDP solution leverages AI techniques like Neural Networks, K-Nearest Neighbors (K-NN), Decision Trees, Random Forests, Support Vector Machines (SVM), and Naïve Bayes. These algorithms accurately predict two-class and multi-class categories, streamlining document categorization and automation.
With techniques such as Linear Regression and Logistic Regression, our IDP solution predicts values and enables data-driven decision-making. By analyzing document patterns and relationships, businesses gain precise forecasting capabilities and actionable insights.
Infrrd's IDP solution employs K-Means clustering to uncover inherent structures within document datasets. This technique automatically groups and segments documents based on similarities, facilitating efficient document management and analysis.
Our IDP solution incorporates advanced anomaly detection techniques. By identifying unusual data points, businesses can proactively manage risks and ensure data quality, improving overall document processing accuracy.
By using AI techniques, we can automate document processing tasks that are typically done manually, such as:
Extracting data from documents
Validating data
Categorizing documents
Routing documents to the right people
Integrating Intelligent Document Processing (IDP) into your enterprise environment is a breeze with Infrrd's flexible approach. We offer two options for IDP integration:
Infrrd understands that different enterprises have unique requirements for their IDP deployment. That's why we offer both cloud and on-premise solutions, providing the flexibility to choose the most suitable option for your business:
When it comes to Infrrd's IDP solution, you can leave your worries about needing a data scientist behind. Our solution is designed to empower businesses without the requirement of having a dedicated data scientist on board. Here's how we make it happen:
It's important to note that while Infrrd's approach eliminates the need for a data scientist, other IDP solutions might have different requirements. When considering alternative vendors, we recommend checking their specific approach to the involvement of data scientists.
At Infrrd, we pride ourselves on delivering a user-centric and accessible IDP solution that puts the power in the hands of your business and process staff. Experience the simplicity and effectiveness of IDP without the need for a data scientist.
Maintaining an IDP system doesn't have to be a challenge. Unlike template-based OCR systems that rely on manual creation and upkeep of document templates, IDP offers a seamless and effortless maintenance experience. Here's why:
Gone are the days of dedicating full-time employees to the creation and maintenance of OCR templates. IDP liberates your workforce from this laborious task, allowing them to focus on more valuable activities.
Experience effortless maintenance with IDP. Say goodbye to template management and embrace a system that evolves with your document processing needs.
The primary objectives of optical character recognition (OCR) are character recognition and text image/picture conversion into editable text. Intelligent document processing (IDP) goes one step higher by combining OCR technology with other intelligent processing methods to automate the workflow and document management process. IDP groups document into distinct categories according to their content, design, or additional features using machine learning algorithms.
Sure. The first step is to schedule a guided demo where you get to jump into the thick of it. After you explore our solution you can try a proof of concept. When you're ready, you can deploy the system to one use case. Then more use cases. Then across your enterprise.
We play nice. Our solutions are API-based. Your documents are feed into the solution using APIs. And extracted data is sent out through APIs. We use REST APIs.
Our solution is cloud-native but is also design for premise deployments. Your choice on how you want to deploy it.
Glad you asked. Our data extraction process runs on servers. We have found performance and accuracy decline when running on a desktop or mobile device. (Remember Infrrd is running a powerful AI stack).
Common documents and use cases work out of the box. The cool thing is your solution will improve as the system learns from your documents upfront and over time.
Did you know no system is 100% accurate all the time? When extraction errors occur you want to correct them. We provide a simple UI that your business analyst will use to make corrections.
Our solution excels at data extraction from handwriting. We've got proprietary methods and techniques that do the trick. It's pretty cool. See for yourself.
See our platform in action to experience the transformative efficiencies it can bring to your processes
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