In this paper, a vision-based patient identification recognition system based on image content analysis and support vector machine is proposed for medical information system, especially in dermatology. This proposed system is composed of three parts: pre-processing, candidate region detection, and digit recognition. To consider the efficiency of the proposed scheme, image normalization is performed. The color information is used to identify camera-captured screen images. In the pre-processing part, the effect of noise in captured screen images is reduced by a bilateral filter. The color and spatial information is used to initially and roughly locate the candidate region. To reduce the skew effect, a skew correction algorithm based on the Hough transform is developed. A template matching algorithm is used to find special symbols for locating the region of interest (ROI). For digit segmentation, digits are segmented in the ROI based on the vertical projection and adaptive thresholding. For the digit recognition, some features are measured from each digit segment and a classifier based on the support vector machine is applied to recognize digits.

The experiment’s results show that the proposed system could effectively not only use color information to distinguish the captured screen images from the skin images but also detect the ROIs. After the digit segmentation, the accuracy rates of digit recognition are 98.4% and 94.2% for the proposed system and the Tesseract Optical Character Recognition (OCR) software, respectively. These results demonstrate that the proposed system outperforms the Tesseract OCR software in terms of the accuracy rate of digit recognition.

本文提出了一种基于图像内容分析和支持向量机的基于视觉的患者身份识别系统，用于医学信息系统，尤其是皮肤病学。该系统由三部分组成：预处理，候选区域检测和数字识别。为了考虑所提出方案的效率，执行图像归一化。颜色信息用于识别摄像机捕获的屏幕图像。在预处理部分中，通过双边滤波器可以减少捕获的屏幕图像中的噪声影响。颜色和空间信息用于初始和大致定位候选区域。为了减少偏斜效应，开发了一种基于霍夫变换的偏斜校正算法。使用模板匹配算法来查找用于定位关注区域（ROI）的特殊符号。对于数字分割，将根据垂直投影和自适应阈值在ROI中对数字进行分割。对于数字识别，从每个数字段中测量一些特征，然后基于支持向量机的分类器应用于识别数字。

实验结果表明，所提出的系统不仅可以有效地利用颜色信息从皮肤图像中区分所捕获的屏幕图像，而且可以检测到ROI。进行数字分割后，所建议的系统和Tesseract光学字符识别（OCR）软件的数字识别准确率分别为98.4％和94.2％。这些结果表明，在数字识别的准确率方面，所提出的系统优于Tesseract OCR软件。