基于FCN的图像中文字目标语义分割  

Semantic Segmentation of Character Targets in Images Based on FCN

作　　者：刘信良 王静秋[1] LIU Xin-Liang;WANG Jing-Qiu(College of Mechanical and Electrical Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing,210016,China)

机构地区：[1]南京航空航天大学机电学院,南京210016

出　　处：《计算机系统应用》2020年第6期175-180,共6页Computer Systems & Applications

摘　　要：本文提出一种基于全卷积神经网络的图像中文字目标语义分割算法和一种新的数据集制作与增广方法.该算法首先采用改进全卷积神经网络对图像中的文字目标进行初步分割,然后利用大津法进行二值化处理,划分出目标的大致区域,最后用全连接条件随机场算法进行修正,得到最终结果.该算法在测试集上准确率为85.7%,速度为0.181秒/幅,为图像目标区域的进一步分析做准备.This study proposes an algorithm for semantic segmentation of targets in images based on fully convolutional neural networks and a new method to make and augment dataset.The algorithm primarily segments the targets from images using improved fully convolutional neural networks,OTSU method is applied to binarize images and segment the general areas of targets,finally,the fully connected conditional random field algorithm is used to correct the general areas of targets and get the final results.This algorithm achieves the accuracy of 85.7%and speed of 0.181 second per image on test set,and prepares for further analysis of targets in images.

关 键 词：语义分割 全卷积神经网络 大津法 全连接条件随机场 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP183[自动化与计算机技术—计算机科学与技术]