Graip.AI is proud to announce the release of our new machine learning model, which offers best-in-class results for document processing. Our model focuses on classifying all document text as keys and values, allowing for easy extraction and understanding of important information. Unlike Microsoft AI Functionality, our model offers superior performance and a customizable interface for active learning and postprocessing improvements. Here’s what you need to know about our technology.
Our main focus is to classify all document’s text as keys and values, making it easier to extract important information from your documents. We have demonstrated good quality and allow support for different languages.
On-Premise Hosting and Legal Compliance
Our model offers on-premise hosting, giving you full control over your customer’s data and security measures. With our technology, customer security standards are satisfied, and there are no dependencies on Microsoft policies. Our legal compliance makes Graip.AI the ideal solution for companies looking to safeguard sensitive information.
Graip.AI allows you to use our generic model as is, or you can opt for custom model retraining on client data. With full control of retraining and postprocessing interfaces, our model provides an opportunity to introduce active learning. Additionally, access to version control eliminates migration problems, making it easier for you to integrate with third-party solutions.
Our document segmentation algorithm is based on text and image elements data as input, including text, boxes, fonts, and location statistics. Using modern element clustering approaches with flexible hyperparameters for tuning, we can define logical blocks on the document page aggregating related information into one segment.
Entity Aggregation and Relations Improvement
Using the document segmentation results, we can aggregate separate values into united entities, preserving important information such as addresses or company requisites that are often located on different rows and may be recognized as several values. Additionally, we can combine long “Other” sections into one solid entity.
Graip.AI vs Microsoft: Which machine learning model is better for document extraction?
When it comes to document extraction, there are a variety of models on the market. In a recent test comparing Graip.AI’s New Generation ML Model with Microsoft’s pre-trained model, we found that both performed well, but there were some noticeable differences in their approaches.
Graip.AI’s model supports different languages and provides on-premise hosting options for added security.
With our model, users have full control over retraining and post-processing, allowing for active learning. Microsoft’s model, on the other hand, does not provide an interface for active learning and post-processing enhancements, making it difficult to predict the model’s results.
Graip.AI’s New Generation ML Model for document processing provides best-in-class results and a customizable interface for active learning and postprocessing improvements. The model’s main focus is to classify all document text as keys and values, making it easier to extract important information from documents, and it supports different languages. Additionally, the on-premise hosting option and legal compliance satisfy customer security standards and safeguard sensitive information. The customizable interface allows users to opt for custom model retraining and postprocessing interfaces with full control, introducing active learning.
The advanced technology behind the document segmentation algorithm, using modern element clustering approaches, defines logical blocks on the document page, aggregating related information into one segment. Entity aggregation and relations improvement preserve important information and combine long sections into one solid entity. Compared to Microsoft’s functionality, Graip.AI’s model consistently adheres to the logic of dividing all document text into key-value pairs, supports different languages, and provides an interface for active learning and post-processing enhancements.