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首页> 外文会议>IEEE International Conference on Advanced Computing >Overlapped Character Recognition: An Innovative Approach
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Overlapped Character Recognition: An Innovative Approach

机译：重叠的性格识别：一种创新方法

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摘要

Immense analysis has been done on optical character recognition (OCR). Numerous works has stated for English, Chinese, Devanagari, Malayalam, Arabic scripts, etc. Segmentation has imp phase in OCR and various articles have been published on different segmentation methods like Thinning, histogram etc for different script during last few years. Generally there is not work done on Overlapped and touching scripts. In this article implemented the latest methods of Overlapped character recognition. Segmentation of Overlapped characters has an extremely strenuous task due to the large variety of characters and their shape, font in the script. It is an important step because inaccurate segmentation will cause errors in recognition. Normalization, binarization and thinning are the pre-processing mechanism used in handwritten character recognition. Proposed Method uses threesholding and Blob Analysis for segmentation and to detect overlapped region using Freeman Chain Code. Finally, we used Support Vector Machine (SVM) for resulting feature vectors and obtain classification performance in the character recognition scheme. An overall performance of 93 % at line and curve set of overlapped images are better than existing methods. Here we have empirically performance of segmentation of overlapped characters with the help of different overlapped images.
机译：已经在光学字符识别（OCR）上进行了巨大的分析。众多作品为英语，中文，Devanagari，Malayalam，阿拉伯语脚本等。分段在OCR中具有IMP阶段，并且在过去几年中，不同的分段方法上发表了各种文章，例如在过去几年中为不同脚本的不同脚本。通常在重叠和触摸脚本上没有工作。在本文中，实施了重叠字符识别的最新方法。由于大量的字符及其形状，脚本中的字体，重叠字符的分割具有极其艰巨的任务。这是一个重要的一步，因为不准确的分割会导致识别中的错误。标准化，二值化和变薄是手写字符识别中使用的预处理机制。所提出的方法使用用于分割的速度和BLOB分析，并使用Freeman链代码检测重叠区域。最后，我们使用了支持向量机（SVM）来产生特征向量，并在字符识别方案中获取分类性能。线路和曲线集重叠图像的整体性能为93％，比现有方法更好。在这里，我们在不同重叠的图像的帮助下具有重叠字符分割的凭证性能。

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