In recent years, intelligent document processing has become a cutting-edge technology that helps companies realise the digital transformation of business processes and automate structured and unstructured data entering from different document sources.
The pandemic has significantly driven the usage of intelligent document processing. It has increased the demand for online network connectivity and purchases of goods. Due to the Allied Market Research report, the global intelligent document processing market is projected to reach 7.4 billion by 2031, growing at a CADR of 21.7% from 2022 to 2031.
We asked the Graip.AI expert – Product Lead Karyna Mihalevich – about intelligent document processing, a new-generation ML model, its features on the example of the Graip.AI model, and how it builds a highly accurate workflow for business.
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What is intelligent document processing?
Karyna Mihalevich: “Intelligent document processing (IDP) commonly combines artificial intelligence (AI) and optical character recognition (OCR) technologies to extract data from documents. IDP produces a high degree of recognition accuracy for most common fonts and supports a variety of digital image file format inputs.
In recent years, IDP has included more intelligent capabilities, such as natural language processing (NLP), computer vision (CV), and speech recognition (SR). In comparison to OCR, these technologies give intelligent document processing more advantages. For example, they add a high level of comprehension in the text conversion process.
Now, modern IDP includes features where the content is used as a part of customer and employee experiences. In comparison to previous solutions, which focused only on text and field data extraction. AI-based document processing makes the content immediately accessible and usable exactly when and where it is needed.”
Karyna Mihalevich, Product Lead, Graip.AI
What is a new-generation ML model?
Karyna Mihalevich: “Machine learning contains a process wherein machines are programmed to learn patterns from data. The learning is based on a set of mathematical rules and statistical assumptions. A common goal in machine learning is to develop a predictive model based on statistical associations among features from a given dataset.
A machine learning model is a file that can be trained to recognise certain types of patterns. You can train the model over a set of data, providing it with an algorithm that it can use to analyse and learn from those data. After you have trained the model, you can use it to analyse new data and make predictions about that data. It can be applied in various business sectors, such as finance, medicine, agriculture, and logistics.
For example, in Graip.AI we based the new-generation ML model on computer vision (CV) and natural language processing (NLP) technologies. It was leveraged for key-value processing (KVP). The model focuses on the document text classification using keys and values (KVPE). It considers the key-value pair as two linked data items where the key is used as a unique identifier for the value.
The new-generation ML model can process semi-structured and unstructured data as input. The model provides data recognition from images or PDF files with or without text layers. These files can include complicated elements such as tables, figures, checkboxes, and handwriting details.”
What are the features of the Graip.AI new-generation ML model?
Karyna Mihalevich: “The Graip.AI new-generation ML model provides significant features for intelligent document processing. They have a noticeable impact on the processed data accuracy level.
The new-generation ML model performs detailed data extraction and captures document headers and items. Also, it can recognise documents in multiple languages. This functionality effectively supports international document processing in a company.
The model is ready to learn Custom document templates. Users can apply regular expressions or intelligent matching settings to process key-value pairs. Also, the solution is flexible and open to non-standard custom scenarios for document processing. The new-generation ML model supports cloud and on-premise hosting. It gives companies absolute control over data and security measures. Also, the model can have no dependencies on the policies of intelligent document processing vendors.
The new-generation ML model has retraining functionality to improve recognition results. Graip.AI with new-generation ML Model is fully flexible with post-processing rules and actions. In addition, Graip.AI provides bulk input processing for companies with big-data volumes.”
How to build highly accurate document processing with the Graip.AI new-generation ML model?
Karyna Mihalevich: “The Graip.AI new-generation ML model is able to read, analyse, classify, extract and evaluate it. Each of these stages helps to strengthen the accuracy of intelligent document processing.
In addition to the high accuracy with the new-generation ML model Graip.AI provides the following functions that ensure the benefits of automated document processing:
- Classification
Documents are classified into different categories. It allows companies, for example, to map data to General Ledger accounts to categorise types of financial transactions. - Document enhancement
Graip.ai converts data into real-time insights by linking internal data with external. - Document reconciliation
Platform compares and cross-checks the information entered into 3-rd party systems against the information presented on uploaded documents (2-way, 3-way matching). It provides account receivable (AR) reconciliation. - Intelligent integration
The ecosystem of the solution consists of accounting software, ERP solutions, and other various products. These elements can work together through API connections. Graip.AI has pre-build integration with the enterprise software SAP S/4HANA 2020. Custom integrations can be performed per request. - Analytics, dashboards, alerts
You can receive real-time insights for business needs without technical intervention. There are multiple dashboards. Also, alerts via email or Slack.
In addition, you can enrich the new-generation ML model functionality with sentiment analysis, named entity recognition, and intelligent search over your data.”
Conclusion
The development of intelligent document processing has taken years to reach this technological and efficiency level which we can observe now. IDP started its development with OCR and continued with advanced NLP and CV technologies. It has enabled the best practices of the IT industry. As a result, businesses can implement IDP solutions that are able to develop document processing without constant programming.
The level of implemented technologies improves along with the level of work accuracy. Through every stage and interaction, ML models improve data correctness and allow businesses to save money for noticeable income. At the classification stage, you can accurately separate documents into different categories to map data to definite accounts. When you go through the integration stage, the IDP ecosystem has all the needed integrations with relevant systems, including ERP and accounting software, for effective data exchange and match.