Businessware Technologies conducted a comprehensive study of seven of the most popular models of intelligent document processing based on artificial intelligence. The purpose of the testing was to determine how effectively these models work out of the box when processing digital invoices, as well as to evaluate their ability to cope with documents with different layouts and languages.
What does model analysis involve?
During the analysis, experts examined several key aspects of the models’ performance. First of all, attention was paid to the accuracy of data recognition; AI benchmark models were assessed by their ability to extract headers, field values, text blocks, and symbols without errors. The second important parameter is the processing speed, which reflects the average time required for the model to work with one document. In addition, the economic component was taken into account: the cost of processing a certain volume of pages and possible additional costs.
The results showed that some models demonstrate high recognition accuracy with a relatively low processing speed, while others provide fast work, but with a slight decrease in accuracy. For invoices with different layouts and languages, differences in the effectiveness of the models were observed, which emphasizes the importance of choosing a solution taking into account specific business problems.
Using Models
Using IDP models can significantly speed up document processing, reduce the risk of errors, and optimize labor costs. However, to achieve maximum efficiency, it is necessary to consider not only the accuracy and speed of processing, but also the specifics of the documents to be processed, including complex layouts and multilingual texts.
Businessware Technologies’ analysis demonstrates that a competent approach to choosing AI models provides a balance between accuracy, speed, and cost of processing, allowing companies to automate document flow and improve the efficiency of business processes. Testing models on real data sets helps identify the strengths and weaknesses of solutions, as well as make informed decisions about implementing certain tools in the corporate environment.
This study also allows companies to predict the costs of document flow automation and choose the best solutions for their tasks. In addition, the testing results serve as a guide for further improving AI models and increasing their performance in real conditions.