Automatic Detection and Segmentation of Text in Low Quality Thai Sign Images
Automatic Detection and Segmentation of Text in Low Quality Thai Sign Images
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低质量泰语标志图像中文本的自动检测和分割
DOI：
10.1109/apccas.2006.342256
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
T. Chalidabhongse
中科院分区：
文献类型：
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作者：
W. Jirattitichareon;T. Chalidabhongse
本文提出了一种用于低质量泰语标志图像中文本自动检测和分割的系统。该方法被设计为实时泰语手语翻译系统的一部分，可用于许多应用。首先，对输入图像进行预处理以提高其质量。其次，我们将LoG（高斯拉普拉斯算子）应用于图像进行边缘检测。边缘检测后，在第三步中，我们执行连通分量标记和形态学操作以进行轮廓填充。为了检测泰语句子中的元素，我们必须设置一些适当的比率，并将它们与每个封闭区域进行比较，以找到辅音、元音、声调和特殊符号。接下来，我们采用 4 行泰语字符标准进行布局分析。最后，我们使用GMM（高斯混合模型）来表示前景和背景，并在选定的颜色模型中进行颜色分割。最后，还执行透视畸变校正方法，为分割的文本做好进一步识别处理的准备。我们在 192 个泰语标志图像上测试了该系统，其中总共包含 4681 个字符。这些图像是在不同照明条件下的多个环境中捕获的。检测准确率90.22%
A system for automatic detection and segmentation of text in low quality Thai sign images is presented in this paper. The method is designed as a part of a real-time Thai sign translator system which can be used in many applications. First, an input image is pre-processed to enhance its quality. Secondly, we apply LoG (Laplacian of Gaussian) to the image for edge detection. After edge detection, in the third step, we perform connected component labeling and morphological operations for contour filling. To detect an element in a Thai sentence, we have to set some appropriate ratios and compare them with each closed region to find consonants, vowels, tones and special symbols. Next, we employ 4-line Thai character criteria for layout analysis. Finally, we use GMM (Gaussian mixture model) to represent foreground and background, and perform color segmentation in selected color model. Finally, a method for perspective distortion correction is also performed to prepare the segmented texts be ready for further recognition process. We tested the system on 192 Thai sign images which contain total of 4681 characters. The images were captured from several environments in various lighting conditions. The detection accuracy is 90.22%