Enhancing low-level feature extraction for small data optical character detection and recognition

• Main Authors: Guan-Yi Wu, 吳冠毅
• Other Authors: Ding-Chang Tseng
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/fnqagp

碩士 === 國立中央大學 === 資訊工程學系 === 107 === Optical character detection and recognition is a traditional problem. Traditional detection and classification need to design algorithms based on image characteristics. Usually, the background or the font shape is changed, we need to redesign new algorithms. The advantage of the traditional algorithms is fast and easier to design suitable algorithms based on the problems. In contrast to deep learning, its versatility is less. Some complicated situations are difficult to extract appropriate features. In deep learning, the network model is like a huge algorithm. We can adjust the parameters of the model through training, and let the model learn as a neural network to know how to deal with complex problems. Thus in this studying, we use deep learning to solve the problems of optical character detection and classification. The difficulty of this application is that the image foreground and background are highly similar. Even if the contrast of images is enhanced, the detection and classification are still difficult due to high noises. So the autonomous learning feature extraction of the convolutional neural network in deep learning is highly help to extract features from these contrast-enhanced images. There are two parts of this paper. The first part discusses the effect of offline image processing on feature extraction. The second part is the online network architecture modification. The tasks include : enlarging the feature receptive field of entire alphabets, changing the residual network architecture to make optimization easier, and saving time in making pre-trained models. In the experiment, the results of the original network architecture and optical character data are poor. The mAP is 67.14%, the recall is 82.52% and the precision is 97.63%. Lots of characters could not be detected. The improvement of this research is significative. We increase the mAP of the whole system about 20% to 97.27%, the recall is increased about 17% to 99.35%, and the precision is increased from 97.63% to 99.60%. The proposed optical character detection and recognition system is near practice on industrial applications.