针对轮胎规格号图像高噪声的特点，本文提出字符截断方式来获得可区分的外轮廓特征，利用BP 神经网络并结合规格号关系特征来识别轮胎规格号。首先，获取可区分规格号字符样本特征：截取字符的三分之二，获取其外轮廓游程特征；随后，对其进行BP 神经网络训练和识别，得到一次识别结果；最后，采用规格号类型特征，对识别结果做二次识别。实验结果表明，BP 神经网络对变形字符有较强的容错能力，特征获取和二次识别算法能够有效识别轮胎规格号。

For big noises tire image, a method of character truncation was presented to get distinguishing outer contourfeatures. Artificial neural network and features of relations was used to recognize tire specifications. First, the samplefeatures of distinguishing specifications were gotten: two-thirds characters were intercepted to get run-lengthcharacteristics of its external contour. Then, one recognition result was gotten by BP neural network training andrecognition. Finally, character features of relations processed fore results were applied to do the second recognition.Experimental results show that the characteristics of acquisition and the second recognition algorithms can effectivelyidentify the tire specifications. Moreover, BP neural network has better fault-tolerance and recognition rate fordeformation characters caused by noises, and feature gotten and secondary identification algorithm can effectivelyidentify tire specifications.