毕业生知识库

首次在中文图像文本的识别任务中应用带有注意力（Attention）机制的编解码（Encoder-Decoder）模型。这种模型在进行文本行识别的同时，对输入和输出之间的对齐关系可以显式地学习。结合卷积神经网络（Convolutional Neural Network，CNN）提取输入图像的特征序列并送入编解码模型，从而完成模型的整体训练，真正意义上实现了端到端的图像文本行识别。

As a direct expression of human semantic information, text plays an important role in daily life. The text in the image can directly reflect its content information, so more and more researchers pay attention to the recognition of text in the image. However, the complex image background and the low resolution of text image bring great challenges to its recognition. In recent years, Deep neural network has developed rapidly and has been successfully applied to many areas. In this paper, the recognition of image text is focused on. And with the aid of end-to-end neural network models the whole text line in the image is recognized. The main achievements of dissertation are as follows:

Presenting a scheme based on the definition criterion to generate text images. The training of Deep Neural Network (DNN) based models needs a huge amount of labeled data. However, labeling text image one by one is costly. Considering the definition of each character position in the image text, the quality of the whole image text is measured. By means of generating a large number of text images, the training of models to recognize image text turns to be realizable.

First introducing the Connectionist Temporal Classification (CTC) objective function to the area of image text recognition. Combining CTC and the Recurrent Neural Network (RNN) to model the whole image text line relieves us of cutting the image text explicitly and achieves better recognition accuracy simultaneously.

Applying the Encoder-Decoder Model with attention mechanism to the task of image text recognition for the first time. Not only can the model recognize the text in the image, but also can learn the alignments between the input and output sequences. The model can be trained with the image feature sequence extracted by Convolutional Neural Network (CNN). Thus truly implementing end-to-end image text recognition.