8 research outputs found
Exploring OCR Capabilities of GPT-4V(ision) : A Quantitative and In-depth Evaluation
This paper presents a comprehensive evaluation of the Optical Character
Recognition (OCR) capabilities of the recently released GPT-4V(ision), a Large
Multimodal Model (LMM). We assess the model's performance across a range of OCR
tasks, including scene text recognition, handwritten text recognition,
handwritten mathematical expression recognition, table structure recognition,
and information extraction from visually-rich document. The evaluation reveals
that GPT-4V performs well in recognizing and understanding Latin contents, but
struggles with multilingual scenarios and complex tasks. Specifically, it
showed limitations when dealing with non-Latin languages and complex tasks such
as handwriting mathematical expression recognition, table structure
recognition, and end-to-end semantic entity recognition and pair extraction
from document image. Based on these observations, we affirm the necessity and
continued research value of specialized OCR models. In general, despite its
versatility in handling diverse OCR tasks, GPT-4V does not outperform existing
state-of-the-art OCR models. How to fully utilize pre-trained general-purpose
LMMs such as GPT-4V for OCR downstream tasks remains an open problem. The study
offers a critical reference for future research in OCR with LMMs. Evaluation
pipeline and results are available at
https://github.com/SCUT-DLVCLab/GPT-4V_OCR
Benchmarking Chinese Text Recognition: Datasets, Baselines, and an Empirical Study
The flourishing blossom of deep learning has witnessed the rapid development
of text recognition in recent years. However, the existing text recognition
methods are mainly proposed for English texts. As another widely-spoken
language, Chinese text recognition (CTR) in all ways has extensive application
markets. Based on our observations, we attribute the scarce attention on CTR to
the lack of reasonable dataset construction standards, unified evaluation
protocols, and results of the existing baselines. To fill this gap, we manually
collect CTR datasets from publicly available competitions, projects, and
papers. According to application scenarios, we divide the collected datasets
into four categories including scene, web, document, and handwriting datasets.
Besides, we standardize the evaluation protocols in CTR. With unified
evaluation protocols, we evaluate a series of representative text recognition
methods on the collected datasets to provide baselines. The experimental
results indicate that the performance of baselines on CTR datasets is not as
good as that on English datasets due to the characteristics of Chinese texts
that are quite different from the Latin alphabet. Moreover, we observe that by
introducing radical-level supervision as an auxiliary task, the performance of
baselines can be further boosted. The code and datasets are made publicly
available at https://github.com/FudanVI/benchmarking-chinese-text-recognitionComment: Code is available at
https://github.com/FudanVI/benchmarking-chinese-text-recognitio