CAJ | 학술논문

　　根据数字字符整体特征，提出一种基于字符整体特征的 Bp 神经网络数字二次识别方法。该方法首先根据Bp神经网络原理对数字字符进行预识别；然后对预识别结果中存在混淆的字符按照字符整体特征进行二次识别，从而准确获得识别结果。该方法结合了神经网络非线性、自主学习特点和字符整体特征形状结构不变性特点，有效的在低样本量情况下，获得较高的字符识别精度。
　　근거수자자부정체특정，제출일충기우자부정체특정적 Bp 신경망락수자이차식별방법。해방법수선근거Bp신경망락원리대수자자부진행예식별；연후대예식별결과중존재혼효적자부안조자부정체특정진행이차식별，종이준학획득식별결과。해방법결합료신경망락비선성、자주학습특점화자부정체특정형상결구불변성특점，유효적재저양본량정황하，획득교고적자부식별정도。
The secondary digital character recognition based on the character global feature and BP neural network is proposed in this paper. First of all, digital characters should be pre-recognized by the method of BP neural network, and a preliminary result can be obtained. After that, the secondary recognition to those characters which may be very close to confusing in the preliminary result should be arranged, so that, those digital characters confused can be distinguished and the better result can be reached. As a result, the method, combined with nonlinear and self-learning of the neural network, and invariance of the overall character feature of the shape and structure, can reach the high recognition accuracy on the condition of the low sample size.