Abstract
This paper introduces the off-road motorcycle Racer number Dataset (RnD), a new challenging dataset for optical character recognition (OCR) research. RnD contains 2,411 images from professional motorsports photographers that depict motorcycle racers in off-road competitions. The images exhibit a wide variety of factors that make OCR difficult, including mud occlusions, motion blur, non-standard fonts, glare, complex backgrounds, etc. The dataset has 5,578 manually annotated bounding boxes around visible motorcycle numbers, along with transcribed digits and letters. Our experiments benchmark leading OCR algorithms and reveal an end-to-end F1 score of only 0.527 on RnD, even after fine-tuning. Analysis of performance on different occlusion types shows mud as the primary challenge, degrading accuracy substantially compared to normal conditions. But the models struggle with other factors including glare, blur, shadows, and dust. Analysis exposes substantial room for improvement and highlights failure cases of existing models. RnD represents a valuable new benchmark to drive innovation in real-world OCR capabilities. The authors hope the community will build upon this dataset and baseline experiments to make progress on the open problem of robustly recognizing text in unconstrained natural environments. The dataset is available at this https URL.
Abstract (translated)
本文介绍了一个新的具有挑战性的摩托车赛车手数据集（RnD），为光学字符识别（OCR）研究提供一个全新的难题。RnD包含了2,411张来自专业摩托车摄影师的专业摩托车赛车手在赛道比赛中的照片。这些照片展示了使OCR困难的各种因素，包括泥泞遮挡、运动模糊、非标准的字体、炫光、复杂的背景等。数据集周围有5,578个手动标注的可见摩托车号码的边界框，以及转录的数字和字母。我们对数据集的性能进行了基准测试，并发现即使在精调后，端到端F1得分也只有0.527。分析不同遮挡类型的性能显示，泥泞是主要挑战，导致准确性大幅下降。但是，模型在包括炫光、模糊、阴影和灰尘等其他因素上表现不佳。分析揭示了很大的改进空间，并突出了现有模型的失败案例。RnD代表了一个有价值的新的挑战，以推动在现实世界中实现OCR能力的创新。作者希望社区将这个数据集和基准实验建立在之上，以解决在不受约束的自然环境中准确识别文本的开放性问题。该数据集的URL为https:// this URL.
URL
https://arxiv.org/abs/2311.09256
