Cards Recognition Systems,(CRSs)are representative computer vision-based applications.They have a broad range of usage scenarios.For example,they can be used to recognize images containing business cards,personal iden...Cards Recognition Systems,(CRSs)are representative computer vision-based applications.They have a broad range of usage scenarios.For example,they can be used to recognize images containing business cards,personal identification cards,and bank cards etc.Even though CRSs have been studied for many years,it is still difficult to recognize cards in camera-based images taken by ordinary devices,e.g.,mobile phones.Diversity of viewpoints and complex backgrounds in the images make the recognition task challenging.Existing systems employing traditional image processing schemes are not robust to varied environment,and are inefficient in dealing with natural images,e.g.,taken by mobile phones.To tackle the problem,we propose a novel framework for card recognition by employing a Convolutional Neutral Network(CNN)based approach.The system localizes the foreground of the image by utilizing a Fully Convolutional Network(FCN).With the help of the foreground map,the system localizes the corners of the card region and employs perspective transformation to alleviate the effects from distortion.Text lines in the card region are detected and recognized by utilizing CNN and Long Short Term Memory,(LSTM).To evaluate the proposed scheme,we collect a large dataset which contains 4,065 images in a variety of shooting scenarios.Experimental results demonstrate the efficacy of the proposed scheme.Specifically,it is able to achieve an accuracy of 90.62%in the end-toend test,outperforming the state-of-the-art.展开更多
电力工程设计中铁塔设计图纸的自动识别与信息提取是一项急需解决的问题。该文提出一种基于深度学习和光学字符识别(Optical Character Recognition,OCR)技术的铁塔设计图纸智能识别系统。该系统由分段结构识别、文本识别和关键信息提取...电力工程设计中铁塔设计图纸的自动识别与信息提取是一项急需解决的问题。该文提出一种基于深度学习和光学字符识别(Optical Character Recognition,OCR)技术的铁塔设计图纸智能识别系统。该系统由分段结构识别、文本识别和关键信息提取3个主要模块组成。分段结构识别模块采用改进的U-Net卷积神经网络模型;文本识别模块基于Tesseract 4.0进行优化,提高字符识别准确率;关键信息提取模块则使用基于规则的解析引擎,从识别出的分段结构和文本中抽取关键信息。实验结果表明,该系统在铁塔图纸识别的准确性、泛化性和效率方面均达到较高水平塔形结构识别F1值为96.35%,字符识别准确率为99.10%。该系统可有效支持电力工程设计和管理的数字化、智能化转型,具有广阔的应用前景。展开更多
基金This work was supported by the National Natural Science Foundation of China(Grant No.61702046)National Key R&D Program of China(Grant No.2017YFB1401500 and 2017YFB1400800).
文摘Cards Recognition Systems,(CRSs)are representative computer vision-based applications.They have a broad range of usage scenarios.For example,they can be used to recognize images containing business cards,personal identification cards,and bank cards etc.Even though CRSs have been studied for many years,it is still difficult to recognize cards in camera-based images taken by ordinary devices,e.g.,mobile phones.Diversity of viewpoints and complex backgrounds in the images make the recognition task challenging.Existing systems employing traditional image processing schemes are not robust to varied environment,and are inefficient in dealing with natural images,e.g.,taken by mobile phones.To tackle the problem,we propose a novel framework for card recognition by employing a Convolutional Neutral Network(CNN)based approach.The system localizes the foreground of the image by utilizing a Fully Convolutional Network(FCN).With the help of the foreground map,the system localizes the corners of the card region and employs perspective transformation to alleviate the effects from distortion.Text lines in the card region are detected and recognized by utilizing CNN and Long Short Term Memory,(LSTM).To evaluate the proposed scheme,we collect a large dataset which contains 4,065 images in a variety of shooting scenarios.Experimental results demonstrate the efficacy of the proposed scheme.Specifically,it is able to achieve an accuracy of 90.62%in the end-toend test,outperforming the state-of-the-art.
文摘电力工程设计中铁塔设计图纸的自动识别与信息提取是一项急需解决的问题。该文提出一种基于深度学习和光学字符识别(Optical Character Recognition,OCR)技术的铁塔设计图纸智能识别系统。该系统由分段结构识别、文本识别和关键信息提取3个主要模块组成。分段结构识别模块采用改进的U-Net卷积神经网络模型;文本识别模块基于Tesseract 4.0进行优化,提高字符识别准确率;关键信息提取模块则使用基于规则的解析引擎,从识别出的分段结构和文本中抽取关键信息。实验结果表明,该系统在铁塔图纸识别的准确性、泛化性和效率方面均达到较高水平塔形结构识别F1值为96.35%,字符识别准确率为99.10%。该系统可有效支持电力工程设计和管理的数字化、智能化转型,具有广阔的应用前景。
