An effective scene to text conversion and its pronunciation is realized. An intelligent combination of Discrete Wavelet Transform (DWT), Contrast Limited Adaptive Histogram Equalization (CLAHE), Wiener filter and adaptive weighted average is utilized for the image enhancement. Subsequently, the Maximally Stable Extremal Region (MSER) is used to detect the text regions. Afterward, the geometrical and contour based approaches filter out the non-text MSERs. The connected component concept is used to group the text candidates. In next step the Optical Character Recognition (OCR) recognizes the text. The Microsoft speech to text synthesizer pronounces the extracted text. The system applicability is tested by using the standard robust reading competition dataset. The designed method secures 93% precision in text segmentation and 89.9% precision in end-to-end recognition.

中文翻译：

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DWT CLAHE 和维纳滤波器的组合用于有效的场景到文本转换和发音

实现了一个有效的文本转换场景及其发音。离散小波变换 (DWT)、对比度限制自适应直方图均衡 (CLAHE)、维纳滤波器和自适应加权平均的智能组合用于图像增强。随后，最大稳定极值区域 (MSER) 用于检测文本区域。然后，基于几何和轮廓的方法过滤掉非文本 MSER。连接组件概念用于对文本候选​​进行分组。在下一步中，光学字符识别 (OCR) 会识别文本。Microsoft 语音到文本合成器对提取的文本进行发音。系统适用性通过使用标准的鲁棒阅读比赛数据集进行测试。所设计的方法确保了 93% 的文本分割精度和 89%。