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首页> 外文期刊>International Journal of Pattern Recognition and Artificial Intelligence >Research on Data Acquisition Algorithms Based on Image Processing and Artificial Intelligence
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Research on Data Acquisition Algorithms Based on Image Processing and Artificial Intelligence

机译：基于图像处理和人工智能的数据采集算法研究

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摘要

At present, image recognition processing technology has been playing a decisive role in the field of pattern recognition, of which automatic recognition of bank notes is an important research topic. Due to the limitation of the size of bill layout and printing method, many invoice layouts are not clear, skewed or distorted, and even there are irregular handwritten signature contents, which lead to the problem of recognition of digital characters on bill surface. In this regard, this paper proposes a data acquisition and recognition algorithm based on improved BP neural network for ticket number identification, which is based on the theory of image processing and recognition, combined with improved bill information recognition technology. First, in the pre-processing stage of bill image, denoising and graying of bill image are processed. After binarization of bill image, the tilt detection method based on Bresenham integer algorithm is used to correct the tilted bill image. Secondly, character localization and feature extraction are carried out for par characters, and the target background is separated from the interference background in order to extract the desired target characters. Finally, the improved BP neural network-based bill digit data acquisition and recognition algorithm is used to realize the classification and recognition of bill characters. The experimental results show that the improved method has better classification and recognition effect than other data acquisition and recognition algorithms.
机译：目前，图像识别处理技术在模式识别领域一直在扮演决定性的作用，其中自动识别银行票据是一个重要的研究主题。由于票据布局和印刷方法的规模的限制，许多发票布局并不清晰，倾斜或扭曲，甚至存在不规则的手写签名内容，这导致账单面上的数字角色识别问题。在这方面，本文提出了一种基于改进的BP神经网络的数据采集和识别算法，用于基于图像处理和识别理论，结合改进的账单信息识别技术。首先，在纸币图像的预处理阶段，处理纸币图像的去噪和灰色。在纸张图像二值后，使用基于Bresenham整数算法的倾斜检测方法来校正倾斜的纸币图像。其次，对PAR字符执行字符本地化和特征提取，并且目标背景与干扰背景分离以提取所需的目标字符。最后，使用改进的基于BP神经网络的账单数字数据采集和识别算法来实现账单字符的分类和识别。实验结果表明，改进的方法具有比其他数据采集和识别算法更好的分类和识别效果。

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