Intelligent Document Processing (IDP) refers to a suite of tools and solutions utilizing deep learning techniques to automate document processing. By leveraging advanced artificial intelligence and computer vision technologies, IDP can manage various types of documents—including email text, PDFs, and scans—and convert them into structured data. The IDP automates the extraction and processing of information within these documents, understanding their content and context, and making the extracted data readily usable for relevant processes or departments.

IDP differs from traditional Optical Character Recognition (OCR) solutions. While OCR primarily focuses on converting scanned documents into machine-readable text, IDP solutions go a step further by not only reading documents but also extracting, classifying, and exporting relevant data—such as key-value pairs, tables, and images—to facilitate further processing or actions based on the results. This advanced functionality is made possible through the synergy of various technologies, including OCR, Computer Vision, Natural Language Processing (NLP), and Robotic Process Automation (RPA). When used in tandem, these technologies enable the highest levels of automation.

Many companies often lack the specific skills needed to develop Intelligent Document Processing (IDP) solutions, which can adversely affect system performance. IDP solutions address these skill gaps by consolidating essential technologies and expertise into a single, user-friendly product that is easy to integrate.

These solutions are designed to be 'non-invasive' and can be seamlessly integrated into existing business applications and platforms. They frequently offer a pre-configured range of ready-to-use solutions, as well as options for more complex and customized implementations. The availability of pre-built use cases allows companies to automate or enhance processes in significantly less time compared to traditional solutions, reducing timelines from several months to just a few days. Additionally, the total cost of ownership for these solutions is greatly diminished, requiring minimal data for setup and minimal effort for integration.

Some common pre-configured use cases include invoice processing, customer onboarding, mortgage processing, contract management, and purchase receipt handling.

Intelligent Document Processing: key phases

Pre-processing of the image

In many Intelligent Document Processing (IDP) solutions, the first step involves pre-processing the image of the received document (e.g., via scan or email). This pre-processing enhances the performance of Optical Character Recognition (OCR) and Intelligent Character Recognition (ICR) algorithms while preserving a "normalized" version of the image. The primary goal of pre-processing is to improve image quality and readability.

Typical pre-processing operations may include image binarization, rotation angle correction, resolution standardization, and various other enhancements. While some solutions may function without this phase, it remains beneficial for maintaining a clearer and more readable version of the document.

Text identification and layout analysis

IDP solutions utilize Computer Vision techniques to understand document structure and identify elements such as text, tables, and images. This phase is typically divided into two key steps:

• Layout Analysis: This step is essential for identifying the document's structure, including paragraphs, headings, tables, and images.
• Optical Character Recognition (OCR): This is crucial for reading the document, enabling further processing as required by the workflow.

During this process, IDP solutions create a machine-readable version of the document, which is then prepared for subsequent automated analysis. Solutions that do not require text for the following steps can either skip or simplify this phase.

Document classification

The document classification process involves automatically assigning each page, or the entire document, to a relevant category. Classification can occur through different methodologies:

• Transcription and Text Analysis: This involves analyzing the text contained within the document.
• Image Analysis: This focuses on examining the visual aspects of the document.
• Hybrid Techniques: These methods involve analyzing both the text and its image.

In the intelligent document processing workflow, both supervised and unsupervised machine learning techniques can be utilized. The unsupervised approach has a lower setup cost since it does not require data labeling; however, it typically offers lower accuracy. Depending on the algorithm used, the model can provide a Confidence Score to represent its certainty regarding its predictions. Based on the technologies employed, this classification phase can be essential for enabling extraction or may be considered an optional step.

Information extraction

Information extraction from the document is a crucial phase necessary for automating processes related to document handling. In this step, the same methodologies used for document classification (analysis of the text, image, or both) can be applied, each with its own advantages and disadvantages.

Among the most time-consuming and costly operations in document processing is the extraction of key information followed by manual data entry. This step aims to automatically transform unstructured data within the document into structured data that can be easily utilized in subsequent phases or underlying processes. During this phase, various types of information can be extracted, including tables, images, signatures, and more.

Result Validation

In most cases, Intelligent Document Processing (IDP) solutions provide a scoring or confidence mechanism that helps review data identified as potentially incorrect. To ensure data accuracy and integrity, IDP platforms leverage human review, external databases, and pre-configured vocabularies to validate the information extracted from documents. This process not only guarantees data quality but also collects improperly processed data, enabling the system's continuous learning (Human in the Loop & Continuous Learning).

Result Enrichment

Another important step before making the data usable is the enrichment of the extracted information. Typically, external databases or services are utilized to supplement the data obtained from the document, enhancing it with more detailed and higher-quality information. For example, a lookup on an external system using a company's name can be performed to assess the company’s status or health.

Integration

A final important aspect involves integrating with the systems from which the data originates and those that will utilize it. Often, IDP systems provide straightforward no-code integration options, either implemented directly through the platform or with the assistance of RPA tools. This phase is essential for simplifying the integration process and ensuring a seamless end-to-end solution that interfaces effectively with the business tools in use.

Benefits

Intelligent Document Processing (IDP) solutions provide companies with numerous benefits:

• Direct Cost Savings: By leveraging scalable and high-performance architectures, IDP solutions significantly reduce both time and costs, minimizing the effort required to process large volumes of data.
• Reduction of Repetitive Manual Activities: With the help of Artificial Intelligence, the need for manual intervention in document processing is minimized.
• Superior Data Quality: Continuous learning and validation enhance data quality, drastically reducing the likelihood of errors.
• Quick Data Processing Start-Up: IDP solutions can be easily integrated through RPA mechanisms, often 5-10 times faster and easier to implement than traditional approaches.
• Ability to Process Any Document: AI enables the management of structured, semi-structured, and unstructured documents of all types.
• Full Automation: The seamless integration of IDP with other areas of the company facilitates the creation of a fully integrated RPA solution without the need for costly updates.
• Improved Productivity: IDP solutions help organizations boost productivity by reducing the time spent on repetitive tasks, ultimately enhancing the quality of the work environment.
• Ease of Use for Businesses: With pre-packaged use cases available, launching and integrating common scenarios becomes simpler and faster.

‍myBiros and benefits

MyBiros is an Intelligent Document Processing solution that enables the automatic processing of documents. Its core functionalities include information extraction and automatic classification of documents. MyBiros provides a prebuilt set of ready-to-use APIs for the most common use cases, along with the ability to retrain the entire pipeline - both the OCR engine and the document interpretation system - for custom applications.

Integrating MyBiros into any application is straightforward, thanks to its APIs and easy interaction with RPA systems. By leveraging advanced deep learning techniques that analyze multimodal features, MyBiros can process all types of documents with a single solution. The system utilizes pre-trained models and data augmentation techniques, allowing it to be trained with a reduced volume of data. This enables automation even for processes involving small volumes of documents.

This solution includes a scoring mechanism that helps reduce false positives by allowing the review of low-confidence data, thereby minimizing errors. Interaction with human users enables the correction of system errors while continuing to train the model, preventing the recurrence of past mistakes (Human in the Loop and continuous learning). Additionally, the high scalability of the cloud-based architecture allows for the processing of highly variable document volumes without the need to allocate expensive resources in advance.

The features mentioned above enable MyBiros to perform optimally on any document, facilitating the automation of a wide range of processes. If you’re curious to learn how MyBiros can simplify document processing, contact us. We're ready to help!