Handwritten character recognition is considered challenging compared with machine-printed characters due to the different human writing styles.Arabic is morphologically rich,and its characters have a high similarity.T...Handwritten character recognition is considered challenging compared with machine-printed characters due to the different human writing styles.Arabic is morphologically rich,and its characters have a high similarity.The Arabic language includes 28 characters.Each character has up to four shapes according to its location in the word(at the beginning,middle,end,and isolated).This paper proposed 12 CNN architectures for recognizing handwritten Arabic characters.The proposed architectures were derived from the popular CNN architectures,such as VGG,ResNet,and Inception,to make them applicable to recognizing character-size images.The experimental results on three well-known datasets showed that the proposed architectures significantly enhanced the recognition rate compared to the baseline models.The experiments showed that data augmentation improved the models’accuracies on all tested datasets.The proposed model outperformed most of the existing approaches.The best achieved results were 93.05%,98.30%,and 96.88%on the HIJJA,AHCD,and AIA9K datasets.展开更多
This paper briefly introduces the main ideas of a sustainable development OCR system based on open architecture techniques and then describes the construction of an optical character recognition (OCR) center built on ...This paper briefly introduces the main ideas of a sustainable development OCR system based on open architecture techniques and then describes the construction of an optical character recognition (OCR) center built on computer clusters, for the purpose of dynamically improving the recognition precision of the digitized texts of a million volumes of books produced by the China-US Million Books Digital Library (CADAL) Project. The practice of this center will provide helpful reference for other digital library projects.展开更多
This study aims to review the latest contributions in Arabic Optical Character Recognition(OCR)during the last decade,which helps interested researchers know the existing techniques and extend or adapt them accordingl...This study aims to review the latest contributions in Arabic Optical Character Recognition(OCR)during the last decade,which helps interested researchers know the existing techniques and extend or adapt them accordingly.The study describes the characteristics of the Arabic language,different types of OCR systems,different stages of the Arabic OCR system,the researcher’s contributions in each step,and the evaluationmetrics for OCR.The study reviews the existing datasets for the Arabic OCR and their characteristics.Additionally,this study implemented some preprocessing and segmentation stages of Arabic OCR.The study compares the performance of the existing methods in terms of recognition accuracy.In addition to researchers’OCRmethods,commercial and open-source systems are used in the comparison.The Arabic language is morphologically rich and written cursive with dots and diacritics above and under the characters.Most of the existing approaches in the literature were evaluated on isolated characters or isolated words under a controlled environment,and few approaches were tested on pagelevel scripts.Some comparative studies show that the accuracy of the existing Arabic OCR commercial systems is low,under 75%for printed text,and further improvement is needed.Moreover,most of the current approaches are offline OCR systems,and there is no remarkable contribution to online OCR systems.展开更多
In today’s digital era,the text may be in form of images.This research aims to deal with the problem by recognizing such text and utilizing the support vector machine(SVM).A lot of work has been done on the English l...In today’s digital era,the text may be in form of images.This research aims to deal with the problem by recognizing such text and utilizing the support vector machine(SVM).A lot of work has been done on the English language for handwritten character recognition but very less work on the under-resourced Hindi language.A method is developed for identifying Hindi language characters that use morphology,edge detection,histograms of oriented gradients(HOG),and SVM classes for summary creation.SVM rank employs the summary to extract essential phrases based on paragraph position,phrase position,numerical data,inverted comma,sentence length,and keywords features.The primary goal of the SVM optimization function is to reduce the number of features by eliminating unnecessary and redundant features.The second goal is to maintain or improve the classification system’s performance.The experiment included news articles from various genres,such as Bollywood,politics,and sports.The proposed method’s accuracy for Hindi character recognition is 96.97%,which is good compared with baseline approaches,and system-generated summaries are compared to human summaries.The evaluated results show a precision of 72%at a compression ratio of 50%and a precision of 60%at a compression ratio of 25%,in comparison to state-of-the-art methods,this is a decent result.展开更多
The optical character recognition for the right to left and cursive languages such as Arabic is challenging and received little attention from researchers in the past compared to the other Latin languages.Moreover,the...The optical character recognition for the right to left and cursive languages such as Arabic is challenging and received little attention from researchers in the past compared to the other Latin languages.Moreover,the absence of a standard publicly available dataset for several low-resource lan-guages,including the Pashto language remained a hurdle in the advancement of language processing.Realizing that,a clean dataset is the fundamental and core requirement of character recognition,this research begins with dataset generation and aims at a system capable of complete language understanding.Keeping in view the complete and full autonomous recognition of the cursive Pashto script.The first achievement of this research is a clean and standard dataset for the isolated characters of the Pashto script.In this paper,a database of isolated Pashto characters for forty four alphabets using various font styles has been introduced.In order to overcome the font style shortage,the graphical software Inkscape has been used to generate sufficient image data samples for each character.The dataset has been pre-processed and reduced in dimensions to 32×32 pixels,and further converted into the binary format with a black background and white text so that it resembles the Modified National Institute of Standards and Technology(MNIST)database.The benchmark database is publicly available for further research on the standard GitHub and Kaggle database servers both in pixel and Comma Separated Values(CSV)formats.展开更多
网络高度发达的信息时代,防止涉密信息被泄露是一件非常重要的任务,尤其是对于政府、军队、公安等重点单位。传统的涉密信息监测系统往往是安装在主机等终端中,无法对于通过手机等智能移动终端偷拍涉密图片或者通过聊天软件上传涉密图...网络高度发达的信息时代,防止涉密信息被泄露是一件非常重要的任务,尤其是对于政府、军队、公安等重点单位。传统的涉密信息监测系统往往是安装在主机等终端中,无法对于通过手机等智能移动终端偷拍涉密图片或者通过聊天软件上传涉密图片的行为无法进行有效的制止。针对这个问题,设计了一种将CTPN文本检测算法、光学字符识别技术(optical character recognition,OCR)与场景识别、图片传输监控相结合的智能移动终端涉密信息监测系统,可广泛应用于Android移动平台中。该系统通过全局扫描,实时相机监察,社交管控三防一体对失泄密行为进行监控监察,有效防止失泄密事故案件的发生。测试结果显示,该系统不仅可以准确识别涉密图片、监测涉密行为并且处理速度快、占用内存空间小,可以满足涉密单位的基本需求。展开更多
Invoice document digitization is crucial for efficient management in industries.The scanned invoice image is often noisy due to various reasons.This affects the OCR(optical character recognition)detection accuracy.In ...Invoice document digitization is crucial for efficient management in industries.The scanned invoice image is often noisy due to various reasons.This affects the OCR(optical character recognition)detection accuracy.In this paper,letter data obtained from images of invoices are denoised using a modified autoencoder based deep learning method.A stacked denoising autoencoder(SDAE)is implemented with two hidden layers each in encoder network and decoder network.In order to capture the most salient features of training samples,a undercomplete autoencoder is designed with non-linear encoder and decoder function.This autoencoder is regularized for denoising application using a combined loss function which considers both mean square error and binary cross entropy.A dataset consisting of 59,119 letter images,which contains both English alphabets(upper and lower case)and numbers(0 to 9)is prepared from many scanned invoices images and windows true type(.ttf)files,are used for training the neural network.Performance is analyzed in terms of Signal to Noise Ratio(SNR),Peak Signal to Noise Ratio(PSNR),Structural Similarity Index(SSIM)and Universal Image Quality Index(UQI)and compared with other filtering techniques like Nonlocal Means filter,Anisotropic diffusion filter,Gaussian filters and Mean filters.Denoising performance of proposed SDAE is compared with existing SDAE with single loss function in terms of SNR and PSNR values.Results show the superior performance of proposed SDAE method.展开更多
When using the current popular text recognition algorithms such as optical character recognition(OCR)algorithm for text images,the presence of watermarks in text images interferes with algorithm recognition to the ext...When using the current popular text recognition algorithms such as optical character recognition(OCR)algorithm for text images,the presence of watermarks in text images interferes with algorithm recognition to the extent of fuzzy font,which is not conducive to the improvement of the recognition rate.In order to pursue fast and high recognition rate,watermark removal has become a critical problem to be solved.This work studies the watermarking algorithm based on morphological algorithm set and classic image algorithm in computer images.It can not only remove the watermark in a short time,but also keep the form and clarity of the text in the image.The algorithm also meets the requirements that the higher the clarity of image and text,the better the processing effect.It can process the Chinese characters with complex structure,complicated radicals or other characters well.In addition,the algorithm can basically process ordinary size images in 1 s,the efficiency is relatively high.展开更多
Text detection and recognition is a hot topic in computer vision,which is considered to be the further development of the traditional optical character recognition(OCR)technology.With the rapid development of machine ...Text detection and recognition is a hot topic in computer vision,which is considered to be the further development of the traditional optical character recognition(OCR)technology.With the rapid development of machine vision system and the wide application of deep learning algorithms,text recognition has achieved excellent performance.In contrast,detecting text block from complex natural scenes is still a challenging task.At present,many advanced natural scene text detection algorithms have been proposed,but most of them run slow due to the complexity of the detection pipeline and can not be applied to industrial scenes.In this paper,we proposed a CCD based machine vision system for realtime text detection in invoice images.In this system,we applied optimizations from several aspects including the optical system,the hardware architecture,and the deep learning algorithm to improve the speed performance of the machine vision system.The experimental data confirms that the optimization methods can significantly improve the running speed of the machine vision system and make it meeting the real-time text detection requirements in industrial scenarios.展开更多
文摘Handwritten character recognition is considered challenging compared with machine-printed characters due to the different human writing styles.Arabic is morphologically rich,and its characters have a high similarity.The Arabic language includes 28 characters.Each character has up to four shapes according to its location in the word(at the beginning,middle,end,and isolated).This paper proposed 12 CNN architectures for recognizing handwritten Arabic characters.The proposed architectures were derived from the popular CNN architectures,such as VGG,ResNet,and Inception,to make them applicable to recognizing character-size images.The experimental results on three well-known datasets showed that the proposed architectures significantly enhanced the recognition rate compared to the baseline models.The experiments showed that data augmentation improved the models’accuracies on all tested datasets.The proposed model outperformed most of the existing approaches.The best achieved results were 93.05%,98.30%,and 96.88%on the HIJJA,AHCD,and AIA9K datasets.
基金Project supported by China-US Million Books Digital Library Project
文摘This paper briefly introduces the main ideas of a sustainable development OCR system based on open architecture techniques and then describes the construction of an optical character recognition (OCR) center built on computer clusters, for the purpose of dynamically improving the recognition precision of the digitized texts of a million volumes of books produced by the China-US Million Books Digital Library (CADAL) Project. The practice of this center will provide helpful reference for other digital library projects.
文摘This study aims to review the latest contributions in Arabic Optical Character Recognition(OCR)during the last decade,which helps interested researchers know the existing techniques and extend or adapt them accordingly.The study describes the characteristics of the Arabic language,different types of OCR systems,different stages of the Arabic OCR system,the researcher’s contributions in each step,and the evaluationmetrics for OCR.The study reviews the existing datasets for the Arabic OCR and their characteristics.Additionally,this study implemented some preprocessing and segmentation stages of Arabic OCR.The study compares the performance of the existing methods in terms of recognition accuracy.In addition to researchers’OCRmethods,commercial and open-source systems are used in the comparison.The Arabic language is morphologically rich and written cursive with dots and diacritics above and under the characters.Most of the existing approaches in the literature were evaluated on isolated characters or isolated words under a controlled environment,and few approaches were tested on pagelevel scripts.Some comparative studies show that the accuracy of the existing Arabic OCR commercial systems is low,under 75%for printed text,and further improvement is needed.Moreover,most of the current approaches are offline OCR systems,and there is no remarkable contribution to online OCR systems.
文摘In today’s digital era,the text may be in form of images.This research aims to deal with the problem by recognizing such text and utilizing the support vector machine(SVM).A lot of work has been done on the English language for handwritten character recognition but very less work on the under-resourced Hindi language.A method is developed for identifying Hindi language characters that use morphology,edge detection,histograms of oriented gradients(HOG),and SVM classes for summary creation.SVM rank employs the summary to extract essential phrases based on paragraph position,phrase position,numerical data,inverted comma,sentence length,and keywords features.The primary goal of the SVM optimization function is to reduce the number of features by eliminating unnecessary and redundant features.The second goal is to maintain or improve the classification system’s performance.The experiment included news articles from various genres,such as Bollywood,politics,and sports.The proposed method’s accuracy for Hindi character recognition is 96.97%,which is good compared with baseline approaches,and system-generated summaries are compared to human summaries.The evaluated results show a precision of 72%at a compression ratio of 50%and a precision of 60%at a compression ratio of 25%,in comparison to state-of-the-art methods,this is a decent result.
文摘The optical character recognition for the right to left and cursive languages such as Arabic is challenging and received little attention from researchers in the past compared to the other Latin languages.Moreover,the absence of a standard publicly available dataset for several low-resource lan-guages,including the Pashto language remained a hurdle in the advancement of language processing.Realizing that,a clean dataset is the fundamental and core requirement of character recognition,this research begins with dataset generation and aims at a system capable of complete language understanding.Keeping in view the complete and full autonomous recognition of the cursive Pashto script.The first achievement of this research is a clean and standard dataset for the isolated characters of the Pashto script.In this paper,a database of isolated Pashto characters for forty four alphabets using various font styles has been introduced.In order to overcome the font style shortage,the graphical software Inkscape has been used to generate sufficient image data samples for each character.The dataset has been pre-processed and reduced in dimensions to 32×32 pixels,and further converted into the binary format with a black background and white text so that it resembles the Modified National Institute of Standards and Technology(MNIST)database.The benchmark database is publicly available for further research on the standard GitHub and Kaggle database servers both in pixel and Comma Separated Values(CSV)formats.
文摘网络高度发达的信息时代,防止涉密信息被泄露是一件非常重要的任务,尤其是对于政府、军队、公安等重点单位。传统的涉密信息监测系统往往是安装在主机等终端中,无法对于通过手机等智能移动终端偷拍涉密图片或者通过聊天软件上传涉密图片的行为无法进行有效的制止。针对这个问题,设计了一种将CTPN文本检测算法、光学字符识别技术(optical character recognition,OCR)与场景识别、图片传输监控相结合的智能移动终端涉密信息监测系统,可广泛应用于Android移动平台中。该系统通过全局扫描,实时相机监察,社交管控三防一体对失泄密行为进行监控监察,有效防止失泄密事故案件的发生。测试结果显示,该系统不仅可以准确识别涉密图片、监测涉密行为并且处理速度快、占用内存空间小,可以满足涉密单位的基本需求。
文摘Invoice document digitization is crucial for efficient management in industries.The scanned invoice image is often noisy due to various reasons.This affects the OCR(optical character recognition)detection accuracy.In this paper,letter data obtained from images of invoices are denoised using a modified autoencoder based deep learning method.A stacked denoising autoencoder(SDAE)is implemented with two hidden layers each in encoder network and decoder network.In order to capture the most salient features of training samples,a undercomplete autoencoder is designed with non-linear encoder and decoder function.This autoencoder is regularized for denoising application using a combined loss function which considers both mean square error and binary cross entropy.A dataset consisting of 59,119 letter images,which contains both English alphabets(upper and lower case)and numbers(0 to 9)is prepared from many scanned invoices images and windows true type(.ttf)files,are used for training the neural network.Performance is analyzed in terms of Signal to Noise Ratio(SNR),Peak Signal to Noise Ratio(PSNR),Structural Similarity Index(SSIM)and Universal Image Quality Index(UQI)and compared with other filtering techniques like Nonlocal Means filter,Anisotropic diffusion filter,Gaussian filters and Mean filters.Denoising performance of proposed SDAE is compared with existing SDAE with single loss function in terms of SNR and PSNR values.Results show the superior performance of proposed SDAE method.
基金Supported by the National Entrepreneurship Training Fund Project(No.201810573007X)China Scholarship Council Project(No.201708440547)GDPU Higher Education Innovation Strong School Project(No.2017GXJK079,51359055)。
文摘When using the current popular text recognition algorithms such as optical character recognition(OCR)algorithm for text images,the presence of watermarks in text images interferes with algorithm recognition to the extent of fuzzy font,which is not conducive to the improvement of the recognition rate.In order to pursue fast and high recognition rate,watermark removal has become a critical problem to be solved.This work studies the watermarking algorithm based on morphological algorithm set and classic image algorithm in computer images.It can not only remove the watermark in a short time,but also keep the form and clarity of the text in the image.The algorithm also meets the requirements that the higher the clarity of image and text,the better the processing effect.It can process the Chinese characters with complex structure,complicated radicals or other characters well.In addition,the algorithm can basically process ordinary size images in 1 s,the efficiency is relatively high.
文摘Text detection and recognition is a hot topic in computer vision,which is considered to be the further development of the traditional optical character recognition(OCR)technology.With the rapid development of machine vision system and the wide application of deep learning algorithms,text recognition has achieved excellent performance.In contrast,detecting text block from complex natural scenes is still a challenging task.At present,many advanced natural scene text detection algorithms have been proposed,but most of them run slow due to the complexity of the detection pipeline and can not be applied to industrial scenes.In this paper,we proposed a CCD based machine vision system for realtime text detection in invoice images.In this system,we applied optimizations from several aspects including the optical system,the hardware architecture,and the deep learning algorithm to improve the speed performance of the machine vision system.The experimental data confirms that the optimization methods can significantly improve the running speed of the machine vision system and make it meeting the real-time text detection requirements in industrial scenarios.
