From d7dea471913be3c6cf665d5edb43b0254848216d Mon Sep 17 00:00:00 2001 From: echo840 <87795401+echo840@users.noreply.github.com> Date: Fri, 30 Aug 2024 11:32:29 +0800 Subject: [PATCH] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index d3a9dc2..ea15ac7 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# On the Hidden Mystery of OCR in Large Multimodal Models +# OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models > Large models have recently played a dominant role in natural language processing and multimodal vision-language learning. However, their effectiveness in text-related visual tasks remains relatively unexplored. In this paper, we conducted a comprehensive evaluation of Large Multimodal Models, such as GPT4V and Gemini, in various text-related visual tasks including Text Recognition, Scene Text-Centric Visual Question Answering (VQA), Document-Oriented VQA, Key Information Extraction (KIE), and Handwritten Mathematical Expression Recognition (HMER). To facilitate the assessment of Optical Character Recognition (OCR) capabilities in Large Multimodal Models, we propose OCRBench, a comprehensive evaluation benchmark. Our study encompasses 29 datasets, making it the most comprehensive OCR evaluation benchmark available. Furthermore, our study reveals both the strengths and weaknesses of these models, particularly in handling multilingual text, handwritten text, non-semantic text, and mathematical expression recognition. Most importantly, the baseline results showcased in this study could provide a foundational framework for the conception and assessment of innovative strategies targeted at enhancing zero-shot multimodal techniques.