Urdu,a prominent subcontinental language,serves as a versatile means of communication.However,its handwritten expressions present challenges for optical character recognition(OCR).While various OCR techniques have bee...Urdu,a prominent subcontinental language,serves as a versatile means of communication.However,its handwritten expressions present challenges for optical character recognition(OCR).While various OCR techniques have been proposed,most of them focus on recognizing printed Urdu characters and digits.To the best of our knowledge,very little research has focused solely on Urdu pure handwriting recognition,and the results of such proposed methods are often inadequate.In this study,we introduce a novel approach to recognizing Urdu pure handwritten digits and characters using Convolutional Neural Networks(CNN).Our proposed method utilizes convolutional layers to extract important features from input images and classifies them using fully connected layers,enabling efficient and accurate detection of Urdu handwritten digits and characters.We implemented the proposed technique on a large publicly available dataset of Urdu handwritten digits and characters.The findings demonstrate that the CNN model achieves an accuracy of 98.30%and an F1 score of 88.6%,indicating its effectiveness in detecting and classifyingUrdu handwritten digits and characters.These results have far-reaching implications for various applications,including document analysis,text recognition,and language understanding,which have previously been unexplored in the context of Urdu handwriting data.This work lays a solid foundation for future research and development in Urdu language detection and processing,opening up new opportunities for advancement in this field.展开更多
Many early Japanese books record a large amount of information,including historical politics,economics,culture,and so on,which are all valuable legacies.These books are waiting to be reorganized at the moment.However,...Many early Japanese books record a large amount of information,including historical politics,economics,culture,and so on,which are all valuable legacies.These books are waiting to be reorganized at the moment.However,a large amount of the books are described by Kuzushiji,a type of handwriting cursive script that is no longer in use today and only readable by a few experts.Therefore,researchers are trying to detect and recognise the characters from these books through modern techniques.Unfortunately,the characteristics of the Kuzushiji,such as Connect-Separate-characters and Manyvariation,hinder the modern technique assisted re-organisation.Connect-Separatecharacters refer to the case of some characters connecting each other or one character being separated into unconnected parts,which makes character detection hard.Manyvariation is one of the typical characteristics of Kuzushiji,defined as the case that the same character has several variations even if they are written by the same person in the same book at the same time,which increases the difficulty of character recognition.In this sense,this paper aims to construct an early Japanese book reorganisation system by combining image processing and deep learning techniques.The experimentation has been done by testing two early Japanese books.In terms of character detection,the final Recall,Precision and F-value reaches 79.8%,80.3%,and 80.0%,respectively.The deep learning based character recognition accuracy of Top3 reaches 69.52%,and the highest recognition rate reaches 82.57%,which verifies the effectiveness of our proposal.展开更多
Handwritten signature and character recognition has become challenging research topic due to its numerous applications. In this paper, we proposed a system that has three sub-systems. The three subsystems focus on off...Handwritten signature and character recognition has become challenging research topic due to its numerous applications. In this paper, we proposed a system that has three sub-systems. The three subsystems focus on offline recognition of handwritten English alphabetic characters (uppercase and lowercase), numeric characters (0 - 9) and individual signatures respectively. The system includes several stages like image preprocessing, the post-processing, the segmentation, the detection of the required amount of the character and signature, feature extraction and finally Neural Network recognition. At first, the scanned image is filtered after conversion of the scanned image into a gray image. Then image cropping method is applied to detect the signature. Then an accurate recognition is ensured by post-processing the cropped images. MATLAB has been used to design the system. The subsystems are then tested for several samples and the results are found satisfactory at about 97% success rate. The quality of the image plays a vital role as the images of poor or mediocre quality may lead to unsuccessful recognition and verification.展开更多
The paper studies a technique of container image ID recognition In the image preprocessing phase, thresholdiong based on histogram and adaptive thresholding is used in the character segmentation phase, a labeling ...The paper studies a technique of container image ID recognition In the image preprocessing phase, thresholdiong based on histogram and adaptive thresholding is used in the character segmentation phase, a labeling method is used, which is based on connected region, location of character block, location of character line recovery of absent and fragmented characters In the recognition phase, adaptive template match and multiple image results synthesis are used A high recognition rate is obtained展开更多
Due to the diversity of climate and environment in China,the frequent occurrence of extreme rainfall events has brought great challenges to flood prevention.Water level measurement is one of the important research top...Due to the diversity of climate and environment in China,the frequent occurrence of extreme rainfall events has brought great challenges to flood prevention.Water level measurement is one of the important research topics of flood prevention.Recently,the image‐based water level recognition method has become an important part of water level measurement research due to its advantages in easy installation,low cost,and zero need of manual reading.However,there are two mainly shortcomings of the existing imagebased water level recognition methods:(1)severely affected by light intensity and(2)low accuracy of water level recognition for stained water gauges.To solve these two problems,this paper proposes a water level recognition method in consideration of complex scenarios.This method first uses a semantic segmentation convolutional neural network to extract the water gauge mask,and then uses the YOLOv5 object detection network to extract the letter“E”on the water gauge.Based on the character sequence inspection strategy,the algorithm dynamically compensates for the missed detection of characters of stained water gauges.Through a large number of experiments,the proposed water level measurement method has good robustness in complex scenarios,meeting the needs of flash flood defense.展开更多
Health care is an important part of human life and is a right for everyone. One of the most basic human rights is to receive health care whenever they need it. However, this is simply not an option for everyone due to...Health care is an important part of human life and is a right for everyone. One of the most basic human rights is to receive health care whenever they need it. However, this is simply not an option for everyone due to the social conditions in which some communities live and not everyone has access to it. This paper aims to serve as a reference point and guide for users who are interested in monitoring their health, particularly their blood analysis to be aware of their health condition in an easy way. This study introduces an algorithmic approach for extracting and analyzing Complete Blood Count (CBC) parameters from scanned images. The algorithm employs Optical Character Recognition (OCR) technology to process images containing tabular data, specifically targeting CBC parameter tables. Upon image processing, the algorithm extracts data and identifies CBC parameters and their corresponding values. It evaluates the status (High, Low, or Normal) of each parameter and subsequently presents evaluations, and any potential diagnoses. The primary objective is to automate the extraction and evaluation of CBC parameters, aiding healthcare professionals in swiftly assessing blood analysis results. The algorithmic framework aims to streamline the interpretation of CBC tests, potentially improving efficiency and accuracy in clinical diagnostics.展开更多
调度命令是铁路运输调度指挥工作的核心指令,准确、快速地传达调度命令,是保障铁路系统在复杂运营环境中安全、高效、有序运转的前提条件。目前,铁路运输生产中存在诸多书面调度命令传递的场景,需要由人工读取书面调度命令信息,并完成...调度命令是铁路运输调度指挥工作的核心指令,准确、快速地传达调度命令,是保障铁路系统在复杂运营环境中安全、高效、有序运转的前提条件。目前,铁路运输生产中存在诸多书面调度命令传递的场景,需要由人工读取书面调度命令信息,并完成相关数据录入,耗时费力、效率较低,且易出现信息错误录入和漏传等问题。基于书面调度命令的特点,文章研究书面调度命令信息智能识别与提取方法,将图像分割处理与经典光学字符识别(OCR,Optical Character Recognition)算法相结合,增强对表格结构和文本内容的识别能力,更为精确地分割、定位和识别书面调度命令中的文字信息;并结合调度命令模板和用语规范,完成关键信息的提取。实验结果表明,在字符识别前先进行表格分割处理,对于提高调度命令文字识别准确率效果显著,便于后续调度命令结构化数据的自动提取。展开更多
This paper designs a set of semi-automatic intelligent cold chain cargo proximity warning system with wireless data transmission, lightweight Optical Character Recognition identification algorithm framework and electr...This paper designs a set of semi-automatic intelligent cold chain cargo proximity warning system with wireless data transmission, lightweight Optical Character Recognition identification algorithm framework and electronic label automatic warning as the core technology for cold chain dairy Fast Moving Consumer Goods contractors. In terms of hardware, Pulse Frequency Modulation modulation and demodulation are used as the main technology to realize wireless transmission and reception of equipment, and digital electronic tags are added to warn the same batch of upcoming goods. In terms of software, based on Chinese-ocr algorithm, image preprocessing and recognition methods are studied, and an early warning system is designed. So as to realize semi-automatic early warning of cold chain logistics goods.展开更多
文摘Urdu,a prominent subcontinental language,serves as a versatile means of communication.However,its handwritten expressions present challenges for optical character recognition(OCR).While various OCR techniques have been proposed,most of them focus on recognizing printed Urdu characters and digits.To the best of our knowledge,very little research has focused solely on Urdu pure handwriting recognition,and the results of such proposed methods are often inadequate.In this study,we introduce a novel approach to recognizing Urdu pure handwritten digits and characters using Convolutional Neural Networks(CNN).Our proposed method utilizes convolutional layers to extract important features from input images and classifies them using fully connected layers,enabling efficient and accurate detection of Urdu handwritten digits and characters.We implemented the proposed technique on a large publicly available dataset of Urdu handwritten digits and characters.The findings demonstrate that the CNN model achieves an accuracy of 98.30%and an F1 score of 88.6%,indicating its effectiveness in detecting and classifyingUrdu handwritten digits and characters.These results have far-reaching implications for various applications,including document analysis,text recognition,and language understanding,which have previously been unexplored in the context of Urdu handwriting data.This work lays a solid foundation for future research and development in Urdu language detection and processing,opening up new opportunities for advancement in this field.
文摘Many early Japanese books record a large amount of information,including historical politics,economics,culture,and so on,which are all valuable legacies.These books are waiting to be reorganized at the moment.However,a large amount of the books are described by Kuzushiji,a type of handwriting cursive script that is no longer in use today and only readable by a few experts.Therefore,researchers are trying to detect and recognise the characters from these books through modern techniques.Unfortunately,the characteristics of the Kuzushiji,such as Connect-Separate-characters and Manyvariation,hinder the modern technique assisted re-organisation.Connect-Separatecharacters refer to the case of some characters connecting each other or one character being separated into unconnected parts,which makes character detection hard.Manyvariation is one of the typical characteristics of Kuzushiji,defined as the case that the same character has several variations even if they are written by the same person in the same book at the same time,which increases the difficulty of character recognition.In this sense,this paper aims to construct an early Japanese book reorganisation system by combining image processing and deep learning techniques.The experimentation has been done by testing two early Japanese books.In terms of character detection,the final Recall,Precision and F-value reaches 79.8%,80.3%,and 80.0%,respectively.The deep learning based character recognition accuracy of Top3 reaches 69.52%,and the highest recognition rate reaches 82.57%,which verifies the effectiveness of our proposal.
文摘Handwritten signature and character recognition has become challenging research topic due to its numerous applications. In this paper, we proposed a system that has three sub-systems. The three subsystems focus on offline recognition of handwritten English alphabetic characters (uppercase and lowercase), numeric characters (0 - 9) and individual signatures respectively. The system includes several stages like image preprocessing, the post-processing, the segmentation, the detection of the required amount of the character and signature, feature extraction and finally Neural Network recognition. At first, the scanned image is filtered after conversion of the scanned image into a gray image. Then image cropping method is applied to detect the signature. Then an accurate recognition is ensured by post-processing the cropped images. MATLAB has been used to design the system. The subsystems are then tested for several samples and the results are found satisfactory at about 97% success rate. The quality of the image plays a vital role as the images of poor or mediocre quality may lead to unsuccessful recognition and verification.
文摘The paper studies a technique of container image ID recognition In the image preprocessing phase, thresholdiong based on histogram and adaptive thresholding is used in the character segmentation phase, a labeling method is used, which is based on connected region, location of character block, location of character line recovery of absent and fragmented characters In the recognition phase, adaptive template match and multiple image results synthesis are used A high recognition rate is obtained
基金National Key Research and Development Program of China,Grant/Award Number:2023YFC3006700。
文摘Due to the diversity of climate and environment in China,the frequent occurrence of extreme rainfall events has brought great challenges to flood prevention.Water level measurement is one of the important research topics of flood prevention.Recently,the image‐based water level recognition method has become an important part of water level measurement research due to its advantages in easy installation,low cost,and zero need of manual reading.However,there are two mainly shortcomings of the existing imagebased water level recognition methods:(1)severely affected by light intensity and(2)low accuracy of water level recognition for stained water gauges.To solve these two problems,this paper proposes a water level recognition method in consideration of complex scenarios.This method first uses a semantic segmentation convolutional neural network to extract the water gauge mask,and then uses the YOLOv5 object detection network to extract the letter“E”on the water gauge.Based on the character sequence inspection strategy,the algorithm dynamically compensates for the missed detection of characters of stained water gauges.Through a large number of experiments,the proposed water level measurement method has good robustness in complex scenarios,meeting the needs of flash flood defense.
文摘Health care is an important part of human life and is a right for everyone. One of the most basic human rights is to receive health care whenever they need it. However, this is simply not an option for everyone due to the social conditions in which some communities live and not everyone has access to it. This paper aims to serve as a reference point and guide for users who are interested in monitoring their health, particularly their blood analysis to be aware of their health condition in an easy way. This study introduces an algorithmic approach for extracting and analyzing Complete Blood Count (CBC) parameters from scanned images. The algorithm employs Optical Character Recognition (OCR) technology to process images containing tabular data, specifically targeting CBC parameter tables. Upon image processing, the algorithm extracts data and identifies CBC parameters and their corresponding values. It evaluates the status (High, Low, or Normal) of each parameter and subsequently presents evaluations, and any potential diagnoses. The primary objective is to automate the extraction and evaluation of CBC parameters, aiding healthcare professionals in swiftly assessing blood analysis results. The algorithmic framework aims to streamline the interpretation of CBC tests, potentially improving efficiency and accuracy in clinical diagnostics.
文摘调度命令是铁路运输调度指挥工作的核心指令,准确、快速地传达调度命令,是保障铁路系统在复杂运营环境中安全、高效、有序运转的前提条件。目前,铁路运输生产中存在诸多书面调度命令传递的场景,需要由人工读取书面调度命令信息,并完成相关数据录入,耗时费力、效率较低,且易出现信息错误录入和漏传等问题。基于书面调度命令的特点,文章研究书面调度命令信息智能识别与提取方法,将图像分割处理与经典光学字符识别(OCR,Optical Character Recognition)算法相结合,增强对表格结构和文本内容的识别能力,更为精确地分割、定位和识别书面调度命令中的文字信息;并结合调度命令模板和用语规范,完成关键信息的提取。实验结果表明,在字符识别前先进行表格分割处理,对于提高调度命令文字识别准确率效果显著,便于后续调度命令结构化数据的自动提取。
文摘This paper designs a set of semi-automatic intelligent cold chain cargo proximity warning system with wireless data transmission, lightweight Optical Character Recognition identification algorithm framework and electronic label automatic warning as the core technology for cold chain dairy Fast Moving Consumer Goods contractors. In terms of hardware, Pulse Frequency Modulation modulation and demodulation are used as the main technology to realize wireless transmission and reception of equipment, and digital electronic tags are added to warn the same batch of upcoming goods. In terms of software, based on Chinese-ocr algorithm, image preprocessing and recognition methods are studied, and an early warning system is designed. So as to realize semi-automatic early warning of cold chain logistics goods.
