14 Aug How to test intelligent document processing models: main aspects
With data volumes growing rapidly and the need to process it efficiently, intelligent document processing is becoming an integral part of business strategies. BusinesswareTech regularly tests leading IDP models to assess their performance in real-world conditions.
Key testing criteria
Assessment of the AI benchmark model’s ability to accurately extract data from documents, including:
- Headers;
- Field values;
- Text;
- Graphic elements.
Processing time reflects the time it takes for a model to analyze and extract information from a single document, which is an important metric for assessing its performance in real-world conditions. It also takes into account the cost of processing a large volume of documents — for example, the cost of processing thousands of pages with possible additional costs — to assess the economic feasibility of using a particular model. During testing in July 2025, experts assessed IDP models on their ability to extract dimensional and tolerance data from engineering drawings, including solutions such as Gemini 2.5 Flash and ChatGPT o4 mini. In June of the same year, a comparison of table recognition models was conducted, including:
- Amazon Textract;
- Azure Prebuilt Layout;
- GPT-4o API.
Previously, in March 2025, the business analyzed the performance of various models in invoice processing, including the Amazon Analyze Expense API and Azure AI Document Intelligence.
BusinesswareTech’s IDP model testing provides valuable information for organizations looking to optimize document processing processes. Understanding the strengths and weaknesses of different models helps to choose the most suitable solution for specific business problems.
Recognition accuracy is a key indicator of the effectiveness of an AI model, since the quality and reliability of the extracted data depends on it. The higher the accuracy, the less likely it is to make errors and need to manually adjust information. The model must be able to correctly recognize headings, field values, and also take into account the structure and layout of the document to preserve the context and logic of the presented data. In addition, it is important that the system correctly processes various types of text blocks and symbols, which is especially important for complex and non-standard documents. High recognition accuracy allows to significantly improve the automation of business processes and reduce operational risks.
No Comments