This AI Paper from Snowflake Evaluates GPT-4 Models Integrated with OCR and Vision for Enhanced Text and Image Analysis: Advancing Document Understanding

The integration of OCR and visual inputs in GPT-4 models enhances the accuracy of document interpretation by addressing the limitations of text-only models. By combining OCR-recognized text with visual inputs, GPT-4 models can simultaneously process both types of information, enabling a more comprehensive understanding of documents. This approach allows the models to interpret spatial arrangements, visual clues, and textual semantics more effectively, resulting in improved accuracy and performance.

The study conducted by researchers from Snowflake demonstrated significant performance improvements when text and images were used together as input. The GPT-4 Vision Turbo model achieved notably higher ANLS scores on various datasets when both OCR text and images were provided as input, compared to text-only models1. The analysis also revealed that the model benefited more from text-rich elements structured within the document.

Furthermore, the GPT-4 Vision Turbo model outperformed heavier text-only models in most tasks, especially when high-resolution images and OCR text were used. This highlights the importance of image quality and OCR accuracy in enhancing document understanding performance.

In summary, the integration of OCR and visual inputs in GPT-4 models allows for a more comprehensive understanding of documents by combining textual and visual information. This approach leads to improved accuracy and performance in document interpretation tasks.

Large language models (LLMs) like GPT-4 improve document understanding compared to traditional text-only models by integrating visual clues and the spatial arrangement of text. This is achieved by combining OCR-recognized text with visual inputs, allowing the models to simultaneously process both types of information. The integration of visual information and textual semantics enhances the accuracy and performance of document understanding tasks, particularly in tasks such as Document Visual Question Answering (DocVQA), where understanding the context requires seamlessly integrating visual and textual information.

The GPT-4 Vision Turbo model, for example, achieved an ANLS score of 87.4 on DocVQA and 71.9 on InfographicsVQA when both OCR text and images were provided as input. These scores are notably higher than those achieved by text-only models, highlighting the importance of integrating visual information for accurate document understanding. Additionally, the GPT-4 Vision Turbo model outperformed heavier text-only models in most tasks, showcasing the effectiveness of integrating OCR-recognized text with document images.