论文标题
利用机器学习较少发达的语言：乌尔都语文本检测的进度
Leveraging machine learning for less developed languages: Progress on Urdu text detection
论文作者
论文摘要
自然场景图像中的文本检测应用于自动驾驶，老年人和盲人的导航帮助。但是，通常由于缺乏数据资源而阻碍了乌尔都语文本检测的研究。我们已经开发了一个带有乌尔都语文本的场景图像数据集。我们介绍了使用机器学习方法从场景图像中执行乌尔都语文本的检测。我们使用通道增强的最大稳定极端区域（MSER）方法提取文本区域。首先，我们根据其几何特性对文本和噪声进行分类。接下来，我们使用支持向量机进行非文本区域的早期丢弃。为了进一步删除非文本区域，我们使用获得的定向梯度（HOG）特征的直方图，并训练第二个SVM分类器。这改善了场景图像中文本区域检测的总体性能。为了支持有关乌尔都语文本的研究，我们旨在使数据自由地用于研究用途。我们还旨在强调乌尔都语文本检测的挑战和研究差距。
Text detection in natural scene images has applications for autonomous driving, navigation help for elderly and blind people. However, the research on Urdu text detection is usually hindered by lack of data resources. We have developed a dataset of scene images with Urdu text. We present the use of machine learning methods to perform detection of Urdu text from the scene images. We extract text regions using channel enhanced Maximally Stable Extremal Region (MSER) method. First, we classify text and noise based on their geometric properties. Next, we use a support vector machine for early discarding of non-text regions. To further remove the non-text regions, we use histogram of oriented gradients (HoG) features obtained and train a second SVM classifier. This improves the overall performance on text region detection within the scene images. To support research on Urdu text, We aim to make the data freely available for research use. We also aim to highlight the challenges and the research gap for Urdu text detection.
