Guangdong Provincial Key Laboratory of Development and Education for Special Needs Children, Lingnan Normal University, Zhanjiang 524048, China.
School of Computer Science and Intelligence Education, Lingnan Normal University, Zhanjiang 524048, China.
Comput Intell Neurosci. 2022 Aug 8;2022:7201775. doi: 10.1155/2022/7201775. eCollection 2022.
Braille character detection helps communication between normal and visually impaired people. The existing Braille detection methods are all aimed at scanning Braille document images while ignoring natural scene Braille images and CNN shining in the field of pattern recognition is rarely used for Braille detection. Firstly, a natural scene Braille image data set named NSBD was constructed. Then, an anchor-free Braille character detection based on the edge feature was proposed by analyzing that Braille characters in natural scene images that are relatively small in size, and a Braille character is composed of Braille dots that werelocated at the edge region of Braille character. Finally, the performance of the proposed method and other classic methods based on CNN was compared on NSBD. The experimental results show that the proposed method has good performance.
盲文字符检测有助于正常人和视障人士之间的交流。现有的盲文检测方法都是针对扫描盲文文档图像的，而忽略了自然场景盲文图像，并且在模式识别领域大放异彩的 CNN 也很少被用于盲文检测。首先，构建了一个名为 NSBD 的自然场景盲文图像数据集。然后，通过分析自然场景图像中的盲文字符尺寸较小，并且盲文字符由位于盲文字符边缘区域的盲文点组成这两个特点，提出了一种基于边缘特征的无锚点盲文字符检测方法。最后，在 NSBD 数据集上对所提方法与其他基于 CNN 的经典方法的性能进行了比较。实验结果表明，所提方法具有良好的性能。
