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Offline Handwritten Characters Recognition Using Moments Features and Neural Networks
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作者 Mohamed Abaynarh Lahbib Zenkouar 《Computer Technology and Application》 2015年第1期19-29,共11页
In this paper we revise the moment theory for pattern recognition designed, to extract patterns from the noisy character datas, and develop unconstrained handwritten. Amazigh character recognition method based upon or... In this paper we revise the moment theory for pattern recognition designed, to extract patterns from the noisy character datas, and develop unconstrained handwritten. Amazigh character recognition method based upon orthogonal moments and neural networks classifier. We argue that, given the natural flexibility of neural network models and the extent of parallel processing that they allow, our algorithm is a step forward in character recognition. More importantly, following the approach proposed, we apply our system to two different databases, to examine the ability to recognize patterns under noise. We discover overwhelming support for different style of writing. Moreover, this basic conclusion appears to remain valid across different levels of smoothing and insensitive to the nuances of character patterns. Experiments tested the effect of set size on recognition accuracy which can reach 97.46%. The novelty of the proposed method is independence of size, slant, orientation, and translation. The performance of the proposed method is experimentally evaluated and the promising results and findings are presented. Our method is compared to K-NN (k-nearest neighbors) classifier algorithm; results show performances of our method. 展开更多
关键词 Neural network character recognition orthogonal moments pattern recognition.
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Fireworks Optimization with Deep Learning-Based Arabic Handwritten Characters Recognition Model
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作者 Abdelwahed Motwakel Badriyya B.Al-onazi +5 位作者 Jaber S.Alzahrani Ayman Yafoz Mahmoud Othman Abu Sarwar Zamani Ishfaq Yaseen Amgad Atta Abdelmageed 《Computer Systems Science & Engineering》 2024年第5期1387-1403,共17页
Handwritten character recognition becomes one of the challenging research matters.More studies were presented for recognizing letters of various languages.The availability of Arabic handwritten characters databases wa... Handwritten character recognition becomes one of the challenging research matters.More studies were presented for recognizing letters of various languages.The availability of Arabic handwritten characters databases was confined.Almost a quarter of a billion people worldwide write and speak Arabic.More historical books and files indicate a vital data set for many Arab nationswritten in Arabic.Recently,Arabic handwritten character recognition(AHCR)has grabbed the attention and has become a difficult topic for pattern recognition and computer vision(CV).Therefore,this study develops fireworks optimizationwith the deep learning-based AHCR(FWODL-AHCR)technique.Themajor intention of the FWODL-AHCR technique is to recognize the distinct handwritten characters in the Arabic language.It initially pre-processes the handwritten images to improve their quality of them.Then,the RetinaNet-based deep convolutional neural network is applied as a feature extractor to produce feature vectors.Next,the deep echo state network(DESN)model is utilized to classify handwritten characters.Finally,the FWO algorithm is exploited as a hyperparameter tuning strategy to boost recognition performance.Various simulations in series were performed to exhibit the enhanced performance of the FWODL-AHCR technique.The comparison study portrayed the supremacy of the FWODL-AHCR technique over other approaches,with 99.91%and 98.94%on Hijja and AHCD datasets,respectively. 展开更多
关键词 Arabic language handwritten character recognition deep learning classification parameter tuning
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Instance Segmentation of Characters Recognized in Palmyrene Aramaic Inscriptions
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作者 Adéla Hamplová Alexey Lyavdansky +3 位作者 TomášNovák Ondrej Svojše David Franc Arnošt Veselý 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第9期2869-2889,共21页
This study presents a single-class and multi-class instance segmentation approach applied to ancient Palmyrene inscriptions,employing two state-of-the-art deep learning algorithms,namely YOLOv8 and Roboflow 3.0.The go... This study presents a single-class and multi-class instance segmentation approach applied to ancient Palmyrene inscriptions,employing two state-of-the-art deep learning algorithms,namely YOLOv8 and Roboflow 3.0.The goal is to contribute to the preservation and understanding of historical texts,showcasing the potential of modern deep learning methods in archaeological research.Our research culminates in several key findings and scientific contributions.We comprehensively compare the performance of YOLOv8 and Roboflow 3.0 in the context of Palmyrene character segmentation—this comparative analysis mainly focuses on the strengths and weaknesses of each algorithm in this context.We also created and annotated an extensive dataset of Palmyrene inscriptions,a crucial resource for further research in the field.The dataset serves for training and evaluating the segmentation models.We employ comparative evaluation metrics to quantitatively assess the segmentation results,ensuring the reliability and reproducibility of our findings and we present custom visualization tools for predicted segmentation masks.Our study advances the state of the art in semi-automatic reading of Palmyrene inscriptions and establishes a benchmark for future research.The availability of the Palmyrene dataset and the insights into algorithm performance contribute to the broader understanding of historical text analysis. 展开更多
关键词 Optical character recognition instance segmentation Palmyrene ancient languages computer vision
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Optimised CNN Architectures for Handwritten Arabic Character Recognition
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作者 Salah Alghyaline 《Computers, Materials & Continua》 SCIE EI 2024年第6期4905-4924,共20页
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
关键词 Optical character recognition(OCR) handwritten arabic characters deep learning
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Neural Network-Powered License Plate Recognition System Design
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作者 Sakib Hasan Md Nagib Mahfuz Sunny +1 位作者 Abdullah Al Nahian Mohammad Yasin 《Engineering(科研)》 2024年第9期284-300,共17页
The development of scientific inquiry and research has yielded numerous benefits in the realm of intelligent traffic control systems, particularly in the realm of automatic license plate recognition for vehicles. The ... The development of scientific inquiry and research has yielded numerous benefits in the realm of intelligent traffic control systems, particularly in the realm of automatic license plate recognition for vehicles. The design of license plate recognition algorithms has undergone digitalization through the utilization of neural networks. In contemporary times, there is a growing demand for vehicle surveillance due to the need for efficient vehicle processing and traffic management. The design, development, and implementation of a license plate recognition system hold significant social, economic, and academic importance. The study aims to present contemporary methodologies and empirical findings pertaining to automated license plate recognition. The primary focus of the automatic license plate recognition algorithm was on image extraction, character segmentation, and recognition. The task of character segmentation has been identified as the most challenging function based on my observations. The license plate recognition project that we designed demonstrated the effectiveness of this method across various observed conditions. Particularly in low-light environments, such as during periods of limited illumination or inclement weather characterized by precipitation. The method has been subjected to testing using a sample size of fifty images, resulting in a 100% accuracy rate. The findings of this study demonstrate the project’s ability to effectively determine the optimal outcomes of simulations. 展开更多
关键词 Intelligent Traffic Control Systems Automatic License Plate recognition (ALPR) Neural Networks Vehicle Surveillance Traffic Management License Plate recognition Algorithms Image Extraction Character Segmentation Character recognition Low-Light Environments Inclement Weather Empirical Findings Algorithm Accuracy Simulation Outcomes DIGITALIZATION
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Chip Surface Character Recognition Based on OpenCV
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作者 Lihang Yin 《Journal of Electronic Research and Application》 2024年第4期161-167,共7页
Chip surface character recognition is an important part of quality inspection in the field of microelectronics manufacturing.By recognizing the character information on the chip,automated production,quality control,an... Chip surface character recognition is an important part of quality inspection in the field of microelectronics manufacturing.By recognizing the character information on the chip,automated production,quality control,and data collection and analysis can be achieved.This article studies a chip surface character recognition method based on the OpenCV vision library.Firstly,the obtained chip images are preprocessed.Secondly,the template matching method is used to locate the chip position.In addition,the surface characters on the chip are individually segmented,and each character image is extracted separately.Finally,a Support Vector Machine(SVM)is used to classify and recognize characters.The results show that this method can accurately recognize the surface characters of chips and meet the requirements of chip quality inspection. 展开更多
关键词 Template matching Character recognition SVM OPENCV
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A Review of Research on Handwritten Chinese Character Recognition with Multi-Feature Fusion
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作者 Peng Deng Guiying Yang 《Journal of Electronic Research and Application》 2024年第5期109-117,共9页
This paper analyzes the progress of handwritten Chinese character recognition technology,from two perspectives:traditional recognition methods and deep learning-based recognition methods.Firstly,the complexity of Chin... This paper analyzes the progress of handwritten Chinese character recognition technology,from two perspectives:traditional recognition methods and deep learning-based recognition methods.Firstly,the complexity of Chinese character recognition is pointed out,including its numerous categories,complex structure,and the problem of similar characters,especially the variability of handwritten Chinese characters.Subsequently,recognition methods based on feature optimization,model optimization,and fusion techniques are highlighted.The fusion studies between feature optimization and model improvement are further explored,and these studies further enhance the recognition effect through complementary advantages.Finally,the article summarizes the current challenges of Chinese character recognition technology,including accuracy improvement,model complexity,and real-time problems,and looks forward to future research directions. 展开更多
关键词 Chinese character recognition Multi-feature fusion Machine learning
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Arabic Optical Character Recognition:A Review 被引量:1
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作者 Salah Alghyaline 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第6期1825-1861,共37页
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
关键词 Arabic Optical Character recognition(OCR) Arabic OCR software Arabic OCR datasets Arabic OCR evaluation
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A Novel Siamese Network for Few/Zero-Shot Handwritten Character Recognition Tasks
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作者 Nagwa Elaraby Sherif Barakat Amira Rezk 《Computers, Materials & Continua》 SCIE EI 2023年第1期1837-1854,共18页
Deep metric learning is one of the recommended methods for the challenge of supporting few/zero-shot learning by deep networks.It depends on building a Siamese architecture of two homogeneous Convolutional Neural Netw... Deep metric learning is one of the recommended methods for the challenge of supporting few/zero-shot learning by deep networks.It depends on building a Siamese architecture of two homogeneous Convolutional Neural Networks(CNNs)for learning a distance function that can map input data from the input space to the feature space.Instead of determining the class of each sample,the Siamese architecture deals with the existence of a few training samples by deciding if the samples share the same class identity or not.The traditional structure for the Siamese architecture was built by forming two CNNs from scratch with randomly initialized weights and trained by binary cross-entropy loss.Building two CNNs from scratch is a trial and error and time-consuming phase.In addition,training with binary crossentropy loss sometimes leads to poor margins.In this paper,a novel Siamese network is proposed and applied to few/zero-shot Handwritten Character Recognition(HCR)tasks.The novelties of the proposed network are in.1)Utilizing transfer learning and using the pre-trained AlexNet as a feature extractor in the Siamese architecture.Fine-tuning a pre-trained network is typically faster and easier than building from scratch.2)Training the Siamese architecture with contrastive loss instead of the binary cross-entropy.Contrastive loss helps the network to learn a nonlinear mapping function that enables it to map the extracted features in the vector space with an optimal way.The proposed network is evaluated on the challenging Chars74K datasets by conducting two experiments.One is for testing the proposed network in few-shot learning while the other is for testing it in zero-shot learning.The recognition accuracy of the proposed network reaches to 85.6%and 82%in few-and zero-shot learning respectively.In addition,a comparison between the performance of the proposed Siamese network and the traditional Siamese CNNs is conducted.The comparison results show that the proposed network achieves higher recognition results in less time.The proposed network reduces the training time from days to hours in both experiments. 展开更多
关键词 Handwritten character recognition(HCR) few-shot learning zero-shot learning deep metric learning transfer learning contrastive loss Chars74K datasets
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Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition
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作者 Mohammed Maray Badriyya B.Al-onazi +5 位作者 Jaber S.Alzahrani Saeed Masoud Alshahrani Najm Alotaibi Sana Alazwari Mahmoud Othman Manar Ahmed Hamza 《Computers, Materials & Continua》 SCIE EI 2023年第3期5467-5482,共16页
The recognition of the Arabic characters is a crucial task incomputer vision and Natural Language Processing fields. Some major complicationsin recognizing handwritten texts include distortion and patternvariabilities... The recognition of the Arabic characters is a crucial task incomputer vision and Natural Language Processing fields. Some major complicationsin recognizing handwritten texts include distortion and patternvariabilities. So, the feature extraction process is a significant task in NLPmodels. If the features are automatically selected, it might result in theunavailability of adequate data for accurately forecasting the character classes.But, many features usually create difficulties due to high dimensionality issues.Against this background, the current study develops a Sailfish Optimizer withDeep Transfer Learning-Enabled Arabic Handwriting Character Recognition(SFODTL-AHCR) model. The projected SFODTL-AHCR model primarilyfocuses on identifying the handwritten Arabic characters in the inputimage. The proposed SFODTL-AHCR model pre-processes the input imageby following the Histogram Equalization approach to attain this objective.The Inception with ResNet-v2 model examines the pre-processed image toproduce the feature vectors. The Deep Wavelet Neural Network (DWNN)model is utilized to recognize the handwritten Arabic characters. At last,the SFO algorithm is utilized for fine-tuning the parameters involved in theDWNNmodel to attain better performance. The performance of the proposedSFODTL-AHCR model was validated using a series of images. Extensivecomparative analyses were conducted. The proposed method achieved a maximum accuracy of 99.73%. The outcomes inferred the supremacy of theproposed SFODTL-AHCR model over other approaches. 展开更多
关键词 Arabic language handwritten character recognition deep learning feature extraction hyperparameter tuning
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Baseline Isolated Printed Text Image Database for Pashto Script Recognition
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作者 Arfa Siddiqu Abdul Basit +3 位作者 Waheed Noor Muhammad Asfandyar Khan M.Saeed H.Kakar Azam Khan 《Intelligent Automation & Soft Computing》 SCIE 2023年第7期875-885,共11页
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
关键词 Text-image database optical character recognition(OCR) pashto isolated characters visual recognition autonomous language understanding deep learning convolutional neural network(CNN)
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Recognition of Characters by Adaptive Combination of Classifiers
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作者 王飞 李在铭 《Journal of Electronic Science and Technology of China》 2004年第2期7-9,共3页
In this paper, the visual feature space based on the long Horizontals, the long Verticals, and the radicals are given. An adaptive combination of classifiers, whose coefficients vary with the input pattern, is also pr... In this paper, the visual feature space based on the long Horizontals, the long Verticals, and the radicals are given. An adaptive combination of classifiers, whose coefficients vary with the input pattern, is also proposed. Experiments show that the approach is promising for character recognition in video sequences. 展开更多
关键词 character recognition adaptive combination multiple classifiers
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Review of Optical Character Recognition for Power System Image Based on Artificial Intelligence Algorithm
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作者 Xun Zhang Wanrong Bai Haoyang Cui 《Energy Engineering》 EI 2023年第3期665-679,共15页
Optical Character Recognition(OCR)refers to a technology that uses image processing technology and character recognition algorithms to identify characters on an image.This paper is a deep study on the recognition effe... Optical Character Recognition(OCR)refers to a technology that uses image processing technology and character recognition algorithms to identify characters on an image.This paper is a deep study on the recognition effect of OCR based on Artificial Intelligence(AI)algorithms,in which the different AI algorithms for OCR analysis are classified and reviewed.Firstly,the mechanisms and characteristics of artificial neural network-based OCR are summarized.Secondly,this paper explores machine learning-based OCR,and draws the conclusion that the algorithms available for this form of OCR are still in their infancy,with low generalization and fixed recognition errors,albeit with better recognition effect and higher recognition accuracy.Finally,this paper explores several of the latest algorithms such as deep learning and pattern recognition algorithms.This paper concludes that OCR requires algorithms with higher recognition accuracy. 展开更多
关键词 Optical character recognition artificial intelligence power system image artificial neural network machine leaning deep learning
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Traffic light detection and recognition in intersections based on intelligent vehicle
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作者 张宁 何铁军 +1 位作者 高朝晖 黄卫 《Journal of Southeast University(English Edition)》 EI CAS 2008年第4期517-521,共5页
To ensure revulsive driving of intelligent vehicles at intersections, a method is presented to detect and recognize the traffic lights. First, the stabling siding at intersections is detected by applying Hough transfo... To ensure revulsive driving of intelligent vehicles at intersections, a method is presented to detect and recognize the traffic lights. First, the stabling siding at intersections is detected by applying Hough transformation. Then, the colors of traffic lights are detected with color space transformation. Finally, self-associative memory is used to recognize the countdown characters of the traffic lights. Test results at 20 real intersections show that the ratio of correct stabling siding recognition reaches up to 90%;and the ratios of recognition of traffic lights and divided characters are 85% and 97%, respectively. The research proves that the method is efficient for the detection of stabling siding and is robust enough to recognize the characters from images with noise and broken edges. 展开更多
关键词 intelligent vehicle stabling siding detection traffic lights detection self-associative memory light-emitting diode (LED) characters recognition
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A Development of a Knowledge Management System for Water Situation Reports in Thailand
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作者 Prattana Deeprasertkul 《Journal of Computer and Communications》 2024年第10期24-36,共13页
The development of a knowledge management system for the National Hydro Data Center of Thailand was described in this paper. The system was created after the major flood event in 2011 to improve water resource managem... The development of a knowledge management system for the National Hydro Data Center of Thailand was described in this paper. The system was created after the major flood event in 2011 to improve water resource management. It addresses the need for easy access to water situation reports, which are crucial for informed decision-making on water usage, allocation, and reservoir management. The system utilizes Optical Character Recognition technique to convert scanned water situation reports into searchable text. It applied FastText and ElasticSearch for advanced search functionalities. FastText identified the documents related to the search query, even with typos or misspelled words. ElasticSearch allows for efficient searching of text data based on relevance. The system also integrates Google Search for additional information access. Therefore, this knowledge management system provides an efficient way to access and analyze water situation data in Thailand. 展开更多
关键词 Optical Character recognition Text Similarity FastText ElasticSearch
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Automated Extraction and Analysis of CBC Test from Scanned Images
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作者 Iman S. Alansari 《Journal of Software Engineering and Applications》 2024年第2期129-141,共13页
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
关键词 Image Processing Optical Character recognition Tesseract OCR Health Care Application
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SVM Model Selection Using PSO for Learning Handwritten Arabic Characters 被引量:2
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作者 Mamouni El Mamoun Zennaki Mahmoud Sadouni Kaddour 《Computers, Materials & Continua》 SCIE EI 2019年第9期995-1008,共14页
Using Support Vector Machine(SVM)requires the selection of several parameters such as multi-class strategy type(one-against-all or one-against-one),the regularization parameter C,kernel function and their parameters.T... Using Support Vector Machine(SVM)requires the selection of several parameters such as multi-class strategy type(one-against-all or one-against-one),the regularization parameter C,kernel function and their parameters.The choice of these parameters has a great influence on the performance of the final classifier.This paper considers the grid search method and the particle swarm optimization(PSO)technique that have allowed to quickly select and scan a large space of SVM parameters.A comparative study of the SVM models is also presented to examine the convergence speed and the results of each model.SVM is applied to handwritten Arabic characters learning,with a database containing 4840 Arabic characters in their different positions(isolated,beginning,middle and end).Some very promising results have been achieved. 展开更多
关键词 SVM PSO handwritten Arabic grid search character recognition
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A novel license plate recognition method using HTD and VTD features 被引量:2
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作者 Zhang Xiangdong Shen Peiyi Li Liangchao Wang Wei Bai Jianhua Zhang Wenbo 《Engineering Sciences》 EI 2010年第1期71-76,共6页
In this paper, a novel method of licence plate recognition (LPR) using the vertical traverse density (VTD) and horizontal traverse density (HTD) is presented. The neutral network algorithm using VTD and HTD features i... In this paper, a novel method of licence plate recognition (LPR) using the vertical traverse density (VTD) and horizontal traverse density (HTD) is presented. The neutral network algorithm using VTD and HTD features is also an innovation. In addition, a so called secondary recognition method which splits characters into different parts is developed. Experimental results show that it is a simple and fast algorithm, which meets the request of real time and nicety performances of LPR and thus has applied value in intelligence transportation system (ITS). 展开更多
关键词 license plate recognition character segment character recognition VTD and HTD features
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An on-line free handwritten Chinese character recognition method based on component cascaded HMMs 被引量:1
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作者 Zhao Wei(赵巍) Liu Jiafeng Tang Xianglong 《High Technology Letters》 EI CAS 2005年第3期301-305,共5页
This paper presents a cascaded Hidden Markov Model (HMM), which allows state's transition, skip and duration. The cascaded HMM extends the way of HMM pattern description of Handwritten Chinese Character (HCC) and... This paper presents a cascaded Hidden Markov Model (HMM), which allows state's transition, skip and duration. The cascaded HMM extends the way of HMM pattern description of Handwritten Chinese Character (HCC) and depicts the behavior of handwritten curve more reliably in terms of the statistic probability. Hence character segmentation and labeling are unnecessary. Viterbi algorithm is integrated in the cascaded HMM after the whole sample sequence of a HCC is input. More than 26,000 component samples are used tor training 407 handwritten component HMMs. At the improved training stage 94 models of 94 Chinese characters are gained by 32,000 samples, Compared with the Segment HMMs approach, the recognition rate of this model tier the tirst candidate is 87.89% and the error rate could be reduced by 12.4%. 展开更多
关键词 chinese character recognition handwritten component HMM cascaded model
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CHARACTER DETECTION AND RECOGNITION SYSTEM OF BEER BOTTLES 被引量:1
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作者 Shen Bangxing Wu Wenjun +2 位作者 Zhang Yepeng Shen Gang Yang Liangen 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2005年第3期467-469,共3页
An optical imaging system and a configuration characteristic algorithm are presented to reduce the difficulties in extracting intact characters image with weak contrast, in recognizing characters on fast moving beer b... An optical imaging system and a configuration characteristic algorithm are presented to reduce the difficulties in extracting intact characters image with weak contrast, in recognizing characters on fast moving beer bottles. The system consists of a hardware subsystem, including a rotating device, CCD, 16 mm focus lens, a frame grabber card, a penetrating lighting and a computer, and a software subsystem. The software subsystem performs pretreatment, character segmentation and character recognition. In the pretreatment, the original image is filtered with preset threshold to remove isolated spots. Then the horizontal projection and the vertical projection are used respectively to retrieve the character segmentation. Subsequently, the configuration characteristic algorithm is applied to recognize the characters. The experimental results demonstrate that this system can recognize the characters on beer bottles accurately and effectively; the algorithm is proven fast, stable and robust, making it suitable in the industrial environment. 展开更多
关键词 Optical imaging system Raised character recognition Configuration characteristic algorithm
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