Handwritten digits recognition has high practical value in the field of image processing and pattern recognition. In order to im-prove the recognition speed,at the premise of high recognition accuracy,a fast handwritten digits recognition method based on SVM is proposed. The new method which uses the separability measure between classes in the feature space to choose the best kernel parameters, can train SVM classifiers fast to recognize the handwritten digits. Due to the computation of separability measure is a simple iterative process,the time required for computing is far less than the time required for training SVM classifiers in traditional parameter optimization methods. Thus,the time for kernel parameters selection will be reduced greatly. Accordingly,the training speed will be increased,and so that the process of recognizing handwritten digits will also be speeded up,while ensuring better classification accuracy. The experiment re-sults of testing MNIST show that the improved algorithm is feasible and effective.%手写体数字识别是图像处理与模式识别中具有较高实用价值的研究热点之一。在保证较高识别精度的前提下，为提高手写体数字的识别速度，提出了一种基于SVM的快速手写体数字识别方法。该方法通过各类别在特征空间中的可分性强度确定SVM最优核参数，快速训练出SVM分类器对手写体数字进行分类识别。由于可分性强度的计算是一个简单的迭代过程，所需时间远小于传统参数优化方法中训练相应SVM分类器所需时间，故参数确定时间被大大缩减，训练速度得到相应提高，从而加快了手写体数字的识别过程，同时保证了较好的分类准确率。通过对MNIST手写体数字库的实验验证，结果表明该算法是可行有效的。
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