CASIA OpenIR  > 毕业生  > 硕士学位论文
Thesis Advisor刘成林
Degree Grantor中国科学院大学
Place of Conferral北京
Keyword自然场景文本检测 自然场景文本提取 最大稳定极值区域 平面化
在基于连通域的方法中,最大稳定极值区域受到追捧和广泛应用。基于此方法,在本文中,我们提出了一个平面化的最大稳定极值区域方法,该方法能够在不需要训练的情况下,有效快速削减大量重复的最大稳定极值区域,以提高场景文本检测的速度和准确率。在ICDAR 2013鲁棒阅读数据集上,我们的方法能够削减70%冗余的最大稳定极值区域,并且相比传统的最大稳定极值区域,程序运行速度能提升接近一倍。
Other AbstractIn daily life, people always touch a lot of natural scenes. The natural scene contains not only a large amount of graphic information, but also text information. Different from the general visual elements, the text contains rich high-level semantic information, which can help the computer to understand the image more accurately. So image text detection is very important for image understanding. Now, the market application, such as translation software, auto driving, image retrieval, human-computer interaction, augmented reality, etc., all need the machine to understand the text information in natural scenes. Therefore, the accurate and efficient detection of text information in the scene has become an urgent need of the market, and it is also one of the important research areas in the field of document analysis and recognition.
    At present, the main methods and techniques of text detection for natural scene images are Region-Based Method, Connected Component-Based Method and Deep Learning Method.
Among the methods proposed so far, the maximally stable extremal region (MSER) method, as a connected component based one, has been pursued and applied widely. In this paper, we propose an efficient method, called flattening method, to quickly prune the large number of overlapping MSERs, so as to improve the speed and accuracy of MSER-based scene text detection. On the ICDAR 2013 Robust Reading Dataset, our method can reduce 70% redundant maximally stable extremal region, and compared with the traditional maximally stable extremal region method, the program can run nearly twice as fast.
Compared with other methods, our method only needs training text/non-text connected component classifier, which requires less training samples and does not need too long training time. The reduction of the maximally stable extremal region greatly reduces the computational complexity and improves the computational efficiency. The experimental results can also reach the performance of the state of the art method.
Document Type学位论文
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
邱泉. 自然场景图像文本检测方法研究[D]. 北京. 中国科学院大学,2017.
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