多重構造とした畳み込みニューラルネットワークによる劣化したナンバープレート画像の平仮名認識

抄録

We propose a multistructure convolutional neural network (CNN) for hiragana recognition of remarkably degraded license plate images captured by security cameras for the purpose of criminal investigation. The proposed multistructure CNN can use the optimal resolution image that cannot be used by conventional CNN by processing multiresolution images so that the recognition performance is improved. In many cases, plural candidates are allowed in remarkably degraded license plate character recognition for criminal investigation because it is not realistic to achieve practical level correct rate with a single candidate. The general criterion of practical level recognition accuracy for criminal investigation is whether the method achieves the correct rate of 90 percent by allowing up to the second candidate. Generally, the recognition accuracy of CNN decreases when the degradation estimation is inaccurate, and the CNN is not optimized. Under the condition that the CNN was not optimized, the proposed multistructure CNN could achieve practical level recognition performance while the conventional CNN could not achieve that performance.