Text Detection and Recognition for Natural Scene Images Using Deep Convolutional Neural Networks

Xianyu Wu; Chao Luo; Qian Zhang; Jiliu Zhou; Hao Yang; Yulian Li

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Text Detection and Recognition for Natural Scene Images Using Deep Convolutional Neural Networks

机译：使用深卷积神经网络的自然场景图像的文本检测与识别

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Words are the most indispensable information in human life.It is very important to analyze and understand the meaning of words.Compared with the general visual elements,the text conveys rich and high-level moral information,which enables the computer to better understand the semantic content of the text.With the rapid development of computer technology,great achievements have been made in text information detection and recognition.However,when dealing with text characters in natural scene images,there are still some limitations in the detection and recognition of natural scene images.Because natural scene image has more interference and complexity than text,these factors make the detection and recognition of natural scene image text face many challenges.To solve this problem,a new text detection and recognition method based on depth convolution neural network is proposed for natural scene image in this paper.In text detection,this method obtains high-level visual features from the bottom pixels by ResNet network,and extracts the context features from character sequences by BLSTM layer,then introduce to the idea of faster R-CNN vertical anchor point to find the bounding box of the detected text,which effectively improves the effect of text object detection.In addition,in text recognition task,DenseNet model is used to construct character recognition based on Kares.Finally,the output of Softmax is used to classify each character.Our method can replace the artificially defined features with automatic learning and context-based features.It improves the efficiency and accuracy of recognition,and realizes text detection and recognition of natural scene images.And on the PAC2018 competition platform,the experimental results have achieved good results.

机译：单词是人类生命中最不可或缺的信息。它是分析和理解单词的含义非常重要。文本传达了丰富和高级的道德信息，这使得计算机能够更好地理解语义文本内容。在计算机技术的快速发展中，文本信息检测和识别方面取得了巨大成就。但是，在处理自然场景图像中的文本字符时，自然场景的检测和识别仍有一些局限性由于自然场景图像具有比文本更多的干扰和复杂性，这些因素使自然场景图像文本面临着许多挑战的影响。要解决这个问题，提出了一种基于深度卷积神经网络的新文本检测和识别方法对于本文的自然场景图像。文本检测，此方法从瓶子获得高级视觉功能OM像素由Reset网络，并通过BLSTM层从字符序列中提取上下文特征，然后介绍更快的R-CNN垂直锚点的思想，找到检测到的文本的边界框，从而有效提高文本对象检测的效果。在文本识别任务中，DenSenet模型用于构建基于kares的字符识别。最后，softmax的输出用于对每个字符进行分类。我们的方法可以用自动学习和基于上下文的特征来替换人工定义的功能。提高了识别的效率和准确性，实现了自然场景图像的文本检测和识别。在PAC2018竞争平台上，实验结果取得了良好的效果。

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来源
《计算机、材料和连续体(英文)》 |2019年第007期|P.289-300|共12页
作者
Xianyu Wu; Chao Luo; Qian Zhang; Jiliu Zhou; Hao Yang; Yulian Li;
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作者单位

School of Computer Science Chengdu University of Information Technology Chengdu 610225 China;

School of Computer Science Chengdu University of Information Technology Chengdu 610225 China;

School of Computer Science University of Nottingham Jubilee Campus NG81BB UK;

School of Computer Science Chengdu University of Information Technology Chengdu 610225 China;

School of Computer Science Chengdu University of Information Technology Chengdu 610225 ChinaSchool of Information and Software Engineering University of Electronic Science and Technology of China Chengdu 610054 China;

School of Computer Science Chengdu University of Information Technology Chengdu 610225 China;

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原文格式 PDF
正文语种 chi
中图分类计算技术、计算机技术;
关键词
Detection; recognition; resnet; blstm; faster R-CNN; densenet;

机译：检测;识别;Reset;BLSTM;更快的R-CNN;DENSENET;

Text Detection and Recognition for Natural Scene Images Using Deep Convolutional Neural Networks

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