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This paper presents a comprehensive evaluation of the Optical CharacterRecognition (OCR) capabilities of the recently released GPT-4V(ision), a LargeMultimodal Model (LMM). We assess the model's performance across a range of OCRtasks, including scene text recognition, handwritten text recognition,handwritten mathematical expression recognition, table structure recognition,and information extraction from visually-rich document. The evaluation revealsthat GPT-4V performs well in recognizing and understanding Latin contents, butstruggles with multilingual scenarios and complex tasks. Specifically, itshowed limitations when dealing with non-Latin languages and complex tasks suchas handwriting mathematical expression recognition, table structurerecognition, and end-to-end semantic entity recognition and pair extractionfrom document image. Based on these observations, we affirm the necessity andcontinued research value of specialized OCR models. In general, despite itsversatility in handling diverse OCR tasks, GPT-4V does not outperform existingstate-of-the-art OCR models. How to fully utilize pre-trained general-purposeLMMs such as GPT-4V for OCR downstream tasks remains an open problem. The studyoffers a critical reference for future research in OCR with LMMs. Evaluationpipeline and results are available athttps://github.com/SCUT-DLVCLab/GPT-4V_OCR.

Exploring OCR Capabilities of GPT-4V(ision) : A Quantitative and In-depth Evaluation