CASIA OpenIR  > 毕业生  > 硕士学位论文
Thesis Advisor刘成林
Degree Grantor中国科学院大学
Place of Conferral北京
Keyword场景文字识别 过切分 递归神经网络
2、提出了一种基于递归神经网络(Recurrent Neural Network, RNN)的文本行识别方法。本文在标准RNN的基础上采用长短时记忆(Long Short Term Memory, LSTM)模块替换神经网络中的隐层节点,并将标准的RNN 扩展到双向网络以更好地捕捉文本行中的上下文信息,进一步结合序列化梯度方向直方图特征,在场景图像数字串识别中取得了较好的结果。
Other Abstract
Text recognition is one of the core branches of pattern recognition. In recent years, the subproblem of scene text recognition has drawn great attention from many researchers and received intensive study. Text recognition in scene images faces unique challenges compared to printed document recognition and handwritten recognition. The background in scene images are more complicated and the image quality is often affected by illumination and the resolution. Oriented to English word recognition and numeric string recognition in scene images, this thesis studies character over-segmentation and text line recognition methods. Our efforts and contributions are divided into two parts:
1.We propose a multi-layer percepetrons(MLP) based over-segmentation method. We utilize the high discrimination ability of neural networks to detect segmentation points between characters in a sliding window manner. This method largely improves the precision and recall rates of segmentation points, and results in higher recognition accuracy of scene text than existing methods on some benchmark datasets.
2.We propose a Recurrent Neural Network(RNN) based method to recognize  text lines in scene images. Specifically, we substitute the hidden neurons of standard RNN by the long short term memory blocks, and expand the network to a bidirectional model. Further, we combine the RNN with serialized HOG features and achieve promising recognition results on numeric strings.
Document Type学位论文
Recommended Citation
GB/T 7714
贺欣. 自然场景文字切分和文本行识别方法研究[D]. 北京. 中国科学院大学,2016.
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