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

• 作者: Chao Luo Hao Yang Jiliu Zhou Qian Zhang Xianyu Wu Yulian Li
• 来源期刊: 计算机、材料和连续体(英文)
• 学科: 工学
• 关键词: Detection RECOGNITION resnet blstm FASTER R-CNN densenet
• 年，卷（期）: 2019,（7）
• 页码范围: 289-300
• 页数: 12页
• 分类号: TP3

摘要

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.