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Research on Recognition Method of Handwritten Numerals Segmentation based on B-P Neural Network

机译:基于B-P神经网络的手写数字分割识别方法研究

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We propose a binarization method based pigment in the ZIP code of 24 bmp image simulation and digital identification by CCD sensors, were extracted the grid binary image of zip code box and message of the two characters binary image; analyze the image processing, which includes code frame edge detection and separation of the image binarization, denoising smoothing, tilt correction, the extraction code number, position, normalization processing, digital image thinning, character recognition feature extraction. Through testing, the recognition rate of this method can be over 90%. The recognition time of characters for character is less than 1.3 second, which means the method is of more effective recognition ability and can better satisfy the real system requirements.
机译:我们提出了一种基于二值化方法在24BMP图像仿真和CCD传感器的数字识别的邮政编码中的颜料,提取了邮政编码框的网格二进制图像和两个字符二进制图像的消息;分析图像处理,包括代码帧边缘检测和图像二值化的分离,去噪平滑,倾斜校正,提取代码编号,位置,归一化处理,数字图像变薄,字符识别特征提取。通过测试,该方法的识别率可以超过90%。字符的字符识别时间小于1.3秒,这意味着该方法具有更有效的识别能力,并且可以更好地满足真实的系统要求。

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