基于MSER和CNN的场景图像文本检测与识别

Savita Choudhary, N. Singh, Sanjay Chichadwani
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引用次数: 8

摘要

自然图像中的文本检测和识别对于从图像中提取信息非常重要,但也是一项非常具有挑战性的任务。本文提出了一种利用最大稳定极值区域(MSER)从自然场景图像中检测文本区域并使用自训练神经网络进行文本识别的方法。对图像进行一些预处理,然后使用MSER和canny边缘来定位更可能包含文本的较小区域。在二值图像上通过简单的算法将文本作为单个字符单独分离出来,然后通过专门为模糊和未对齐字符设计的识别模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Text Detection and Recognition from Scene Images using MSER and CNN
Detection and recognition of text from natural images is very important for extracting information from images but is an extensively challenging task. This paper proposes an approach for detection of text area from natural scene images using Maximally Stable Extremal Regions (MSER) and recognizing the text using a self-trained Neural Network. Some preprocessing is applied to the image then MSER and canny edge is used to locate the smaller areas that may more likely contain text. The text is individually isolated as single characters by simple algorithms on the binary image and then passed through the recognition model specially designed for hazy and unaligned characters.
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