BACKGROUND Older adults with mild cognitive impairment(MCI)often show motor dysfunction,including slower gait and impaired handwriting.While gait and handwriting parameters are promising for MCI screening,their combin...BACKGROUND Older adults with mild cognitive impairment(MCI)often show motor dysfunction,including slower gait and impaired handwriting.While gait and handwriting parameters are promising for MCI screening,their combined potential to distinguish MCI from cognitively normal adults is unclear.AIM To assess gait and handwriting differences and their potential for screening MCI in older adults.METHODS Ninety-five participants,including 34 with MCI and 61 cognitively normal controls,were assessed for gait using the GAITRite^(R)system and handwriting with a dot-matrix pen.Five machine learning models were developed to assess the discriminative power of gait and handwriting data for MCI screening.RESULTS Compared to the cognitively normal group,the MCI group had slower gait velocity(Z=-2.911,P=0.004),shorter stride and step lengths(t=-3.005,P=0.003;t=2.863,P=0.005),and longer cycle,standing,and double support times(t=-2.274,P=0.025;t=-2.376,P=0.018;t=-2.717,P=0.007).They also had reduced cadence(t=2.060,P=0.042)and increased double support time variability(Z=-2.614,P=0.009).In handwriting,the MCI group showed lower average pressure(all tasks:Z=-2.135,P=0.033)and decreased accuracy(graphic task:Z=-2.447,P=0.014;Chinese character task:Z=-3.078,P=0.002).In the graphic task,they demonstrated longer time in air(Z=-2.865,P=0.004),reduced X-axis maximum velocities(Z=-3.237,P=0.001),and lower accelerations(X-axis:Z=-2.880,P=0.004;Y-axis:Z=-1.987,P=0.047)and maximum accelerations(X-axis:Z=-3.998,P<0.001;Y-axis:Z=-2.050,P=0.040).The multimodal analysis achieved the highest accuracy(74.4%)with the Gradient Boosting Classifier.CONCLUSION Integrating gait and handwriting kinematics parameters provides a viable method for distinguishing MCI,potentially supporting large-scale screening,especially in resource-limited settings.展开更多
Handwriting is a unique and significant human feature that distinguishes them from one another.There are many researchers have endeavored to develop writing recognition systems utilizing specific signatures or symbols...Handwriting is a unique and significant human feature that distinguishes them from one another.There are many researchers have endeavored to develop writing recognition systems utilizing specific signatures or symbols for person identification through verification.However,such systems are susceptible to forgery,posing security risks.In response to these challenges,we propose an innovative hybrid technique for individual identification based on independent handwriting,eliminating the reliance on specific signatures or symbols.In response to these challenges,we propose an innovative hybrid technique for individual identification based on independent handwriting,eliminating the reliance on specific signatures or symbols.Our innovative method is intricately designed,encompassing five distinct phases:data collection,preprocessing,feature extraction,significant feature selection,and classification.One key advancement lies in the creation of a novel dataset specifically tailored for Bengali handwriting(BHW),setting the foundation for our comprehensive approach.Post-preprocessing,we embarked on an exhaustive feature extraction process,encompassing integration with kinematic,statistical,spatial,and composite features.This meticulous amalgamation resulted in a robust set of 91 features.To enhance the efficiency of our system,we employed an analysis of variance(ANOVA)F test and mutual information scores approach,meticulously selecting the most pertinent features.In the identification phase,we harnessed the power of cutting-edge deep learning models,notably the Convolutional Neural Network(CNN)and Bidirectional Long Short-Term Memory(BiLSTM).These models underwent rigorous training and testing to accurately discern individuals based on their handwriting characteristics.Moreover,our methodology introduces a groundbreaking hybrid model that synergizes CNN and BiLSTM,capitalizing on fine motor features for enhanced individual classifications.Crucially,our experimental results underscore the superiority of our approach.The CNN,BiLSTM,and hybrid models exhibited superior performance in individual classification when compared to prevailing state-of-the-art techniques.This validates our method’s efficacy and underscores its potential to outperform existing technologies,marking a significant stride forward in the realm of individual identification through handwriting analysis.展开更多
Individuals with special needs learn more slowly than their peers and they need repetitions to be permanent.However,in crowded classrooms,it is dif-ficult for a teacher to deal with each student individually.This probl...Individuals with special needs learn more slowly than their peers and they need repetitions to be permanent.However,in crowded classrooms,it is dif-ficult for a teacher to deal with each student individually.This problem can be overcome by using supportive education applications.However,the majority of such applications are not designed for special education and therefore they are not efficient as expected.Special education students differ from their peers in terms of their development,characteristics,and educational qualifications.The handwriting skills of individuals with special needs are lower than their peers.This makes the task of Handwriting Recognition(HWR)more difficult.To over-come this problem,we propose a new personalized handwriting verification sys-tem that validates digits from the handwriting of special education students.The system uses a Convolutional Neural Network(CNN)created and trained from scratch.The data set used is obtained by collecting the handwriting of the students with the help of a tablet.A special education center is visited and the handwrittenfigures of the students are collected under the supervision of special education tea-chers.The system is designed as a person-dependent system as every student has their writing style.Overall,the system achieves promising results,reaching a recognition accuracy of about 94%.Overall,the system can verify special educa-tion students’handwriting digits with high accuracy and is ready to integrate with a mobile application that is designed to teach digits to special education students.展开更多
Support vector machine (SVM) has been successfully applied for classification in this paper. This paper discussed the basic principle of the SVM at first, and then SVM classifier with polynomial kernel and the Gaussia...Support vector machine (SVM) has been successfully applied for classification in this paper. This paper discussed the basic principle of the SVM at first, and then SVM classifier with polynomial kernel and the Gaussian radial basis function kernel are choosen to determine pupils who have difficulties in writing. The 10-fold cross-validation method for training and validating is introduced. The aim of this paper is to compare the performance of support vector machine with RBF and polynomial kernel used for classifying pupils with or without handwriting difficulties. Experimental results showed that the performance of SVM with RBF kernel is better than the one with polynomial kernel.展开更多
In recent years,researchers in handwriting recognition analysis relating to indigenous languages have gained significant internet among research communities.The recent developments of artificial intelligence(AI),natur...In recent years,researchers in handwriting recognition analysis relating to indigenous languages have gained significant internet among research communities.The recent developments of artificial intelligence(AI),natural language processing(NLP),and computational linguistics(CL)find useful in the analysis of regional low resource languages.Automatic lexical task participation might be elaborated to various applications in the NLP.It is apparent from the availability of effective machine recognition models and open access handwritten databases.Arabic language is a commonly spoken Semitic language,and it is written with the cursive Arabic alphabet from right to left.Arabic handwritten Character Recognition(HCR)is a crucial process in optical character recognition.In this view,this paper presents effective Computational linguistics with Deep Learning based Handwriting Recognition and Speech Synthesizer(CLDL-THRSS)for Indigenous Language.The presented CLDL-THRSS model involves two stages of operations namely automated handwriting recognition and speech recognition.Firstly,the automated handwriting recognition procedure involves preprocessing,segmentation,feature extraction,and classification.Also,the Capsule Network(CapsNet)based feature extractor is employed for the recognition of handwritten Arabic characters.For optimal hyperparameter tuning,the cuckoo search(CS)optimization technique was included to tune the parameters of the CapsNet method.Besides,deep neural network with hidden Markov model(DNN-HMM)model is employed for the automatic speech synthesizer.To validate the effective performance of the proposed CLDL-THRSS model,a detailed experimental validation process takes place and investigates the outcomes interms of different measures.The experimental outcomes denoted that the CLDL-THRSS technique has demonstrated the compared methods.展开更多
This study aimed to explore the performance of the perceptual-visuomotor skills and the production of handwriting in children with Learning Disabilities.A total of 56 children participated,being a convenience sample,o...This study aimed to explore the performance of the perceptual-visuomotor skills and the production of handwriting in children with Learning Disabilities.A total of 56 children participated,being a convenience sample,of both sexes,average age of eight years old,from 3rd to 5th grade level of Elementary School.The children were divided into the following groups:GI(28 children diagnosed with Learning Disabilities);GII(28 children with good academic performance,paired with GI in relation to chronological age and sex).They were evaluated individually in dysgraphic scale,visual perception development test,and fine motor evaluation.Data analysis was performed.There was a significant difference between GI and GII for the subtests of eye-hand coordination,copying,visual closure,fine motor precision,and fine manual control tests.They had difference between the groups for handwriting performance in descending and/or ascending subtests,irregularity of dimension,poor forms,and total score of Dysgraphia Scale.The results presented in this study indicate that children with Learning Disabilities can manifest significant visomotor impairment and deficit in legibility and handwriting quality,causing failures in the elaboration of sensorimotor plans that,added to the intrinsic deficit of long-term memory,result in persistent academic difficulties.展开更多
Detailed Assessment of Speed of Handwriting (DASH 17+) assessment provides information about the speed and legibility of handwriting. Handwriting difficulties in general and DASH17+ performance, in particular, are sig...Detailed Assessment of Speed of Handwriting (DASH 17+) assessment provides information about the speed and legibility of handwriting. Handwriting difficulties in general and DASH17+ performance, in particular, are signs of neuromotor difficulties. Individualized interventions can be developed with a better understanding of both the biomechanical and neurological underpinnings of the task. We used a multimodal assessment strategy to deconstruct the product and process of handwriting measures in adults. A total of 23 neurotypical college age adults took part in the study. We combined the standardized norm-referenced test DASH17+ and explored the online process of handwriting using the MovAlyzeR software, and simultaneously explored prefrontal cortex activity, using functional near infrared spectroscopy (fNIRS), during the task execution. Our research indicated that underlying neural and kinematic mechanisms changed between tasks, within tasks, and even from one trial block to another that are not reflected in the DASH17+ performance assessment alone. Therefore, this multi-modal approach provides a promising method in clinical populations to further investigate any subtle change in handwriting.展开更多
This paper focuses on a state sharing method for an artificial neural network (ANN) and hidden Markov model (HMM) hybrid on line handwriting recognition system. A modeling precision based distance measure is proposed ...This paper focuses on a state sharing method for an artificial neural network (ANN) and hidden Markov model (HMM) hybrid on line handwriting recognition system. A modeling precision based distance measure is proposed to describe similarity between two ANNs, which are used as HMM state models. Limiting maximum system performance loss, a minimum quantification error aimed hierarchical clustering algorithm is designed to choose the most representative models. The system performance is improved by about 1.5% while saving 40% of the system expense. About 92% of the performance may also be maintained while reducing 70% of system parameters. The suggested method is quite useful for designing pen based interface for various handheld devices.展开更多
This study investigated the effect of a visual memory training program on Chinese handwriting performance among primary school students with dyslexia in Hong Kong. Eight students of Grade 2 to 3 who were diagnosed wit...This study investigated the effect of a visual memory training program on Chinese handwriting performance among primary school students with dyslexia in Hong Kong. Eight students of Grade 2 to 3 who were diagnosed with dyslexia were recruited. All participants received six sessions of training, which composed of 30-minute computerized game-based visual memory training and 30-minute Chinese character segmentation training. Visual perceptual skills and Chinese handwriting performance were assessed before and after the training, as well as three weeks after training using the Test of Visual Perceptual Skills (3rd edition) (TVPS-3) and the Chinese Handwriting Analysis System (CHAS). In comparing the pre- and post-training results, paired t-tests revealed significant improvements in visual memory skills, as well as handwriting speed, pause time and pen pressure after the training. There was no significant improvement in handwriting accuracy or legibility. The improved visual memory and handwriting performance did not show a significant drop at the follow-up assessments. This study showed promising results on a structured program to improve the Chinese handwriting performance, mainly in speed, of primary school children. The improvements appeared to be well-sustained after the training program. There is a need to further study the long-term effect of the program through a randomized controlled trial study.展开更多
A handwriting detecting system based on Micro- accelerometer and Micro-gyros is proposed. And the algorithm of the detecting system is also described in detail. And the error analysis of the detecting system is also d...A handwriting detecting system based on Micro- accelerometer and Micro-gyros is proposed. And the algorithm of the detecting system is also described in detail. And the error analysis of the detecting system is also described in de-tail. The motion contrail of the handwriting de-tecting in the 3-D space can be recognized through compute the matrix of attitude angles and the dynamic information of the handwriting detecting which is mapped on the 2-D plane. Then the information of contrail can be recurred on the writing plane by integral. There were good results in the actual experiment.展开更多
Filling forms is one of the most useful and powerful ways to collect information from people in business, education and many other domains. Nowadays, almost everything is computerized. That creates a curtail need for ...Filling forms is one of the most useful and powerful ways to collect information from people in business, education and many other domains. Nowadays, almost everything is computerized. That creates a curtail need for extracting these handwritings from the forms in order to get them into the computer systems and databases. In this paper, we propose an original method that will extract handwritings from two types of forms;bank and administrative form. Our system will take as input any of the two forms already filled. And according to some statistical measures our system will identify the form. The second step is to subtract the filled form from a previously inserted empty form. In order to make the acting easier and faster a Fourier-Melin transform was used to re-orient the forms correctly. This method has been evaluated with 50 handwriting forms (from both types Bank and University) and the results were approximatively 90%.展开更多
This paper compares two classification methods to determine pupils who have difficulties in writing. Classification experiments are made with neural network and support vector machine method separately. The samples ar...This paper compares two classification methods to determine pupils who have difficulties in writing. Classification experiments are made with neural network and support vector machine method separately. The samples are divided into two groups of writers, below average printers (test group) and above average printers (control group) are applied. The aim of this paper is to demonstrate that neural network and support vector machine can be successfully used in classifying pupils with or without handwriting difficulties. Our results showed that support vector machine classifier yield slightly better percentage than neural network classifier and it has a much stable result.展开更多
Handwriting identification is widely accepted as scientific evidence.However,its authenticity is questioned because it depends on the appraiser's professional skills and susceptibility to deliberate false identifi...Handwriting identification is widely accepted as scientific evidence.However,its authenticity is questioned because it depends on the appraiser's professional skills and susceptibility to deliberate false identification by expert witnesses.Consequently,there is an urgent need for an effective handwriting identification system(HWIS)that reduces reliance on the appraiser's skills and mitigates the risk of international false identification.Here,we report a HWIS that integrates a self-powered handwriting signal data acquisition device with an advanced deep learning architecture possessing powerful feature extraction ability and one-class classification function.The device successfully captures the characteristic differences in handwriting behavior between genuine writers and forgers,and the handwriting identification results demonstrate the excellent performance of our system,showcasing its powerful potential to solve the longstanding challenge of handwriting identification that has perplexed humans for a considerable period.Moreover,this work exhibits the system's capability for remote access and downloading the handwriting signal data through the data cloud,highlighting its practical value for fulfilling the requirements of handwriting recognition and identification applications,and it can effectively advance signature information security and ensure the protection of private information.展开更多
Imitated handwriting and elderly handwriting are two manifestation patterns of altered handwriting.Several similarities in features can be found in both,such as gentle movement and curved jitter.In practice,it is very...Imitated handwriting and elderly handwriting are two manifestation patterns of altered handwriting.Several similarities in features can be found in both,such as gentle movement and curved jitter.In practice,it is very easy to confuse the two patterns,leading to wrong decisions and difficulties in document examination.The key to solving these problems is to recognize the similarities and differences between imitated handwriting and elderly handwriting.This paper comprises four parts.The first part introduces the general features of elderly handwriting;the second part takes up the general characteristics of imitated handwriting;the third part analyzes the common features of imitated handwriting and elderly handwriting;the fourth part draws a conclusion about their key points of identification.Since the number of cases requiring identification of elderly handwriting and imitated handwriting is increasing every year,this paper has practical significance for document examiners and provides theoretical support to questioned document examination.展开更多
Unconstrained offiine handwriting recognition is a challenging task in the areas of document analysis and pattern recognition. In recent years, to sufficiently exploit the supervisory information hidden in document im...Unconstrained offiine handwriting recognition is a challenging task in the areas of document analysis and pattern recognition. In recent years, to sufficiently exploit the supervisory information hidden in document images, much effort has been made to integrate multi-layer perceptrons （MLPs） in either a hybrid or a tandem fashion into hidden Markov models （HMMs）. However, due to the weak learnability of MLPs, the learnt features are not necessarily optimal for subsequent recognition tasks. In this paper, we propose a deep architecture-based tandem approach for unconstrained offiine handwriting recognition. In the proposed model, deep belief networks arc adopted to learn the compact representations of sequential data, while HMMs are applied for （sub-）word recognition. We evaluate the proposed model on two publicly available datasets, i.e., RIMES and IFN/ENIT, which are based on Latin and Arabic languages respectively, and one dataset collected by ourselves called Devanagari （all Indian script）. Extensive experiments show the advantage of the proposed model, especially over the MLP-HMMs taudem approaches.展开更多
As a historic technology,handwriting identification,especially that of Chinese characters,plays an important role in judicial trials both in domestic and abroad.However,in recent decades,handwriting identification has...As a historic technology,handwriting identification,especially that of Chinese characters,plays an important role in judicial trials both in domestic and abroad.However,in recent decades,handwriting identification has feced a challenging situation;the scientificity of handwriting identification has been questioned.This article analyzes the reasons for questioning handwriting identification,focusing on the aspects of standards,methods,quantitative analysis of handwriting identification,and qualification,training,and assessment of document examiners.Besides,corresponding solutions for the above aspects and some other characteristics,such as systematic identification,cross-examination of expert opinions,and rule of corroboration,are proposed.展开更多
Handwriting expertise,as a form of forensic evidence,was once considered by most courts under the Anglo-American law system to be infallible,but this position was significantly challenged by the Daubert case(1993)and ...Handwriting expertise,as a form of forensic evidence,was once considered by most courts under the Anglo-American law system to be infallible,but this position was significantly challenged by the Daubert case(1993)and further by the President’s Council of Advisors on Science and Technology report published in 2016.In China,handwriting expertise has often been accepted as forensic evidence.However,this does not mean that there is no need to review the reliability of handwriting expertise.In this study,we analyze the current situation in China regarding the reliability of handwriting identification using cases from China’s judicial judgment database.We intend to identify the reasons for rejection of handwriting expertise,analyze the outcomes of applications for re-examination,and examine the court’s evaluation of different forensic opinions in relation to a given case.We also propose ways to strengthen the reliability of handwriting identification in China.展开更多
In 2017,the Republic of Kazakhstan began the phased transition of its alphabet from Cyrillic to Latin script.This transition has presented significant challenges to Kazakhstani document examiners,who have yet to devel...In 2017,the Republic of Kazakhstan began the phased transition of its alphabet from Cyrillic to Latin script.This transition has presented significant challenges to Kazakhstani document examiners,who have yet to develop appropriate methodologies for the analysis of handwriting samples written in the Kazakh language using Latin letters.This study aims to identify distinguishing macro and micro features of letters within Kazakh writing samples produced using the Latin alphabet and determine their frequencies of occurrence and discriminating power indices.Micro features were examined using the four most frequently appearing letters:“a”,“y”,“e”and“n”.A comparative analysis of tested Latin letters with those of a similar configuration in Cyrillic demonstrated differences in the number of distinguishing features,as well as in the frequency of occurrence and discriminating power indices of similar features.These results show that separate statistical bases should be used for Latin and Cyrillic letters when analysing handwriting samples based on the frequencies of occurrence of micro and macro writing features.展开更多
基金Supported by National Natural Science Foundation of China,No.72174061 and No.71704053Key Research and Development Program of Zhejiang Province,No.2025C02106+1 种基金China Scholarship Council Foundation,No.202308330251Health Science and Technology Project of Zhejiang Provincial Health Commission,No.2022KY370。
文摘BACKGROUND Older adults with mild cognitive impairment(MCI)often show motor dysfunction,including slower gait and impaired handwriting.While gait and handwriting parameters are promising for MCI screening,their combined potential to distinguish MCI from cognitively normal adults is unclear.AIM To assess gait and handwriting differences and their potential for screening MCI in older adults.METHODS Ninety-five participants,including 34 with MCI and 61 cognitively normal controls,were assessed for gait using the GAITRite^(R)system and handwriting with a dot-matrix pen.Five machine learning models were developed to assess the discriminative power of gait and handwriting data for MCI screening.RESULTS Compared to the cognitively normal group,the MCI group had slower gait velocity(Z=-2.911,P=0.004),shorter stride and step lengths(t=-3.005,P=0.003;t=2.863,P=0.005),and longer cycle,standing,and double support times(t=-2.274,P=0.025;t=-2.376,P=0.018;t=-2.717,P=0.007).They also had reduced cadence(t=2.060,P=0.042)and increased double support time variability(Z=-2.614,P=0.009).In handwriting,the MCI group showed lower average pressure(all tasks:Z=-2.135,P=0.033)and decreased accuracy(graphic task:Z=-2.447,P=0.014;Chinese character task:Z=-3.078,P=0.002).In the graphic task,they demonstrated longer time in air(Z=-2.865,P=0.004),reduced X-axis maximum velocities(Z=-3.237,P=0.001),and lower accelerations(X-axis:Z=-2.880,P=0.004;Y-axis:Z=-1.987,P=0.047)and maximum accelerations(X-axis:Z=-3.998,P<0.001;Y-axis:Z=-2.050,P=0.040).The multimodal analysis achieved the highest accuracy(74.4%)with the Gradient Boosting Classifier.CONCLUSION Integrating gait and handwriting kinematics parameters provides a viable method for distinguishing MCI,potentially supporting large-scale screening,especially in resource-limited settings.
基金MMU Postdoctoral and Research Fellow(Account:MMUI/230023.02).
文摘Handwriting is a unique and significant human feature that distinguishes them from one another.There are many researchers have endeavored to develop writing recognition systems utilizing specific signatures or symbols for person identification through verification.However,such systems are susceptible to forgery,posing security risks.In response to these challenges,we propose an innovative hybrid technique for individual identification based on independent handwriting,eliminating the reliance on specific signatures or symbols.In response to these challenges,we propose an innovative hybrid technique for individual identification based on independent handwriting,eliminating the reliance on specific signatures or symbols.Our innovative method is intricately designed,encompassing five distinct phases:data collection,preprocessing,feature extraction,significant feature selection,and classification.One key advancement lies in the creation of a novel dataset specifically tailored for Bengali handwriting(BHW),setting the foundation for our comprehensive approach.Post-preprocessing,we embarked on an exhaustive feature extraction process,encompassing integration with kinematic,statistical,spatial,and composite features.This meticulous amalgamation resulted in a robust set of 91 features.To enhance the efficiency of our system,we employed an analysis of variance(ANOVA)F test and mutual information scores approach,meticulously selecting the most pertinent features.In the identification phase,we harnessed the power of cutting-edge deep learning models,notably the Convolutional Neural Network(CNN)and Bidirectional Long Short-Term Memory(BiLSTM).These models underwent rigorous training and testing to accurately discern individuals based on their handwriting characteristics.Moreover,our methodology introduces a groundbreaking hybrid model that synergizes CNN and BiLSTM,capitalizing on fine motor features for enhanced individual classifications.Crucially,our experimental results underscore the superiority of our approach.The CNN,BiLSTM,and hybrid models exhibited superior performance in individual classification when compared to prevailing state-of-the-art techniques.This validates our method’s efficacy and underscores its potential to outperform existing technologies,marking a significant stride forward in the realm of individual identification through handwriting analysis.
文摘Individuals with special needs learn more slowly than their peers and they need repetitions to be permanent.However,in crowded classrooms,it is dif-ficult for a teacher to deal with each student individually.This problem can be overcome by using supportive education applications.However,the majority of such applications are not designed for special education and therefore they are not efficient as expected.Special education students differ from their peers in terms of their development,characteristics,and educational qualifications.The handwriting skills of individuals with special needs are lower than their peers.This makes the task of Handwriting Recognition(HWR)more difficult.To over-come this problem,we propose a new personalized handwriting verification sys-tem that validates digits from the handwriting of special education students.The system uses a Convolutional Neural Network(CNN)created and trained from scratch.The data set used is obtained by collecting the handwriting of the students with the help of a tablet.A special education center is visited and the handwrittenfigures of the students are collected under the supervision of special education tea-chers.The system is designed as a person-dependent system as every student has their writing style.Overall,the system achieves promising results,reaching a recognition accuracy of about 94%.Overall,the system can verify special educa-tion students’handwriting digits with high accuracy and is ready to integrate with a mobile application that is designed to teach digits to special education students.
文摘Support vector machine (SVM) has been successfully applied for classification in this paper. This paper discussed the basic principle of the SVM at first, and then SVM classifier with polynomial kernel and the Gaussian radial basis function kernel are choosen to determine pupils who have difficulties in writing. The 10-fold cross-validation method for training and validating is introduced. The aim of this paper is to compare the performance of support vector machine with RBF and polynomial kernel used for classifying pupils with or without handwriting difficulties. Experimental results showed that the performance of SVM with RBF kernel is better than the one with polynomial kernel.
文摘In recent years,researchers in handwriting recognition analysis relating to indigenous languages have gained significant internet among research communities.The recent developments of artificial intelligence(AI),natural language processing(NLP),and computational linguistics(CL)find useful in the analysis of regional low resource languages.Automatic lexical task participation might be elaborated to various applications in the NLP.It is apparent from the availability of effective machine recognition models and open access handwritten databases.Arabic language is a commonly spoken Semitic language,and it is written with the cursive Arabic alphabet from right to left.Arabic handwritten Character Recognition(HCR)is a crucial process in optical character recognition.In this view,this paper presents effective Computational linguistics with Deep Learning based Handwriting Recognition and Speech Synthesizer(CLDL-THRSS)for Indigenous Language.The presented CLDL-THRSS model involves two stages of operations namely automated handwriting recognition and speech recognition.Firstly,the automated handwriting recognition procedure involves preprocessing,segmentation,feature extraction,and classification.Also,the Capsule Network(CapsNet)based feature extractor is employed for the recognition of handwritten Arabic characters.For optimal hyperparameter tuning,the cuckoo search(CS)optimization technique was included to tune the parameters of the CapsNet method.Besides,deep neural network with hidden Markov model(DNN-HMM)model is employed for the automatic speech synthesizer.To validate the effective performance of the proposed CLDL-THRSS model,a detailed experimental validation process takes place and investigates the outcomes interms of different measures.The experimental outcomes denoted that the CLDL-THRSS technique has demonstrated the compared methods.
文摘This study aimed to explore the performance of the perceptual-visuomotor skills and the production of handwriting in children with Learning Disabilities.A total of 56 children participated,being a convenience sample,of both sexes,average age of eight years old,from 3rd to 5th grade level of Elementary School.The children were divided into the following groups:GI(28 children diagnosed with Learning Disabilities);GII(28 children with good academic performance,paired with GI in relation to chronological age and sex).They were evaluated individually in dysgraphic scale,visual perception development test,and fine motor evaluation.Data analysis was performed.There was a significant difference between GI and GII for the subtests of eye-hand coordination,copying,visual closure,fine motor precision,and fine manual control tests.They had difference between the groups for handwriting performance in descending and/or ascending subtests,irregularity of dimension,poor forms,and total score of Dysgraphia Scale.The results presented in this study indicate that children with Learning Disabilities can manifest significant visomotor impairment and deficit in legibility and handwriting quality,causing failures in the elaboration of sensorimotor plans that,added to the intrinsic deficit of long-term memory,result in persistent academic difficulties.
文摘Detailed Assessment of Speed of Handwriting (DASH 17+) assessment provides information about the speed and legibility of handwriting. Handwriting difficulties in general and DASH17+ performance, in particular, are signs of neuromotor difficulties. Individualized interventions can be developed with a better understanding of both the biomechanical and neurological underpinnings of the task. We used a multimodal assessment strategy to deconstruct the product and process of handwriting measures in adults. A total of 23 neurotypical college age adults took part in the study. We combined the standardized norm-referenced test DASH17+ and explored the online process of handwriting using the MovAlyzeR software, and simultaneously explored prefrontal cortex activity, using functional near infrared spectroscopy (fNIRS), during the task execution. Our research indicated that underlying neural and kinematic mechanisms changed between tasks, within tasks, and even from one trial block to another that are not reflected in the DASH17+ performance assessment alone. Therefore, this multi-modal approach provides a promising method in clinical populations to further investigate any subtle change in handwriting.
文摘This paper focuses on a state sharing method for an artificial neural network (ANN) and hidden Markov model (HMM) hybrid on line handwriting recognition system. A modeling precision based distance measure is proposed to describe similarity between two ANNs, which are used as HMM state models. Limiting maximum system performance loss, a minimum quantification error aimed hierarchical clustering algorithm is designed to choose the most representative models. The system performance is improved by about 1.5% while saving 40% of the system expense. About 92% of the performance may also be maintained while reducing 70% of system parameters. The suggested method is quite useful for designing pen based interface for various handheld devices.
文摘This study investigated the effect of a visual memory training program on Chinese handwriting performance among primary school students with dyslexia in Hong Kong. Eight students of Grade 2 to 3 who were diagnosed with dyslexia were recruited. All participants received six sessions of training, which composed of 30-minute computerized game-based visual memory training and 30-minute Chinese character segmentation training. Visual perceptual skills and Chinese handwriting performance were assessed before and after the training, as well as three weeks after training using the Test of Visual Perceptual Skills (3rd edition) (TVPS-3) and the Chinese Handwriting Analysis System (CHAS). In comparing the pre- and post-training results, paired t-tests revealed significant improvements in visual memory skills, as well as handwriting speed, pause time and pen pressure after the training. There was no significant improvement in handwriting accuracy or legibility. The improved visual memory and handwriting performance did not show a significant drop at the follow-up assessments. This study showed promising results on a structured program to improve the Chinese handwriting performance, mainly in speed, of primary school children. The improvements appeared to be well-sustained after the training program. There is a need to further study the long-term effect of the program through a randomized controlled trial study.
文摘A handwriting detecting system based on Micro- accelerometer and Micro-gyros is proposed. And the algorithm of the detecting system is also described in detail. And the error analysis of the detecting system is also described in de-tail. The motion contrail of the handwriting de-tecting in the 3-D space can be recognized through compute the matrix of attitude angles and the dynamic information of the handwriting detecting which is mapped on the 2-D plane. Then the information of contrail can be recurred on the writing plane by integral. There were good results in the actual experiment.
文摘Filling forms is one of the most useful and powerful ways to collect information from people in business, education and many other domains. Nowadays, almost everything is computerized. That creates a curtail need for extracting these handwritings from the forms in order to get them into the computer systems and databases. In this paper, we propose an original method that will extract handwritings from two types of forms;bank and administrative form. Our system will take as input any of the two forms already filled. And according to some statistical measures our system will identify the form. The second step is to subtract the filled form from a previously inserted empty form. In order to make the acting easier and faster a Fourier-Melin transform was used to re-orient the forms correctly. This method has been evaluated with 50 handwriting forms (from both types Bank and University) and the results were approximatively 90%.
文摘This paper compares two classification methods to determine pupils who have difficulties in writing. Classification experiments are made with neural network and support vector machine method separately. The samples are divided into two groups of writers, below average printers (test group) and above average printers (control group) are applied. The aim of this paper is to demonstrate that neural network and support vector machine can be successfully used in classifying pupils with or without handwriting difficulties. Our results showed that support vector machine classifier yield slightly better percentage than neural network classifier and it has a much stable result.
基金supported by the National Natural Science Foundation of China(Grant Nos.52105206,62241308,and 92271201)Guangdong Basic and Applied Basic Research Foundation(2023A1515240050)+1 种基金China Postdoctoral Science Foundation(Grant No.BX2021230)the Opening Project of National and local joint Engineering Research Center for industrial friction and lubrication technology(Grant No.2023-GD-0001).
文摘Handwriting identification is widely accepted as scientific evidence.However,its authenticity is questioned because it depends on the appraiser's professional skills and susceptibility to deliberate false identification by expert witnesses.Consequently,there is an urgent need for an effective handwriting identification system(HWIS)that reduces reliance on the appraiser's skills and mitigates the risk of international false identification.Here,we report a HWIS that integrates a self-powered handwriting signal data acquisition device with an advanced deep learning architecture possessing powerful feature extraction ability and one-class classification function.The device successfully captures the characteristic differences in handwriting behavior between genuine writers and forgers,and the handwriting identification results demonstrate the excellent performance of our system,showcasing its powerful potential to solve the longstanding challenge of handwriting identification that has perplexed humans for a considerable period.Moreover,this work exhibits the system's capability for remote access and downloading the handwriting signal data through the data cloud,highlighting its practical value for fulfilling the requirements of handwriting recognition and identification applications,and it can effectively advance signature information security and ensure the protection of private information.
文摘Imitated handwriting and elderly handwriting are two manifestation patterns of altered handwriting.Several similarities in features can be found in both,such as gentle movement and curved jitter.In practice,it is very easy to confuse the two patterns,leading to wrong decisions and difficulties in document examination.The key to solving these problems is to recognize the similarities and differences between imitated handwriting and elderly handwriting.This paper comprises four parts.The first part introduces the general features of elderly handwriting;the second part takes up the general characteristics of imitated handwriting;the third part analyzes the common features of imitated handwriting and elderly handwriting;the fourth part draws a conclusion about their key points of identification.Since the number of cases requiring identification of elderly handwriting and imitated handwriting is increasing every year,this paper has practical significance for document examiners and provides theoretical support to questioned document examination.
基金the National Natural Science Foundation of China （No. 61403353）
文摘Unconstrained offiine handwriting recognition is a challenging task in the areas of document analysis and pattern recognition. In recent years, to sufficiently exploit the supervisory information hidden in document images, much effort has been made to integrate multi-layer perceptrons （MLPs） in either a hybrid or a tandem fashion into hidden Markov models （HMMs）. However, due to the weak learnability of MLPs, the learnt features are not necessarily optimal for subsequent recognition tasks. In this paper, we propose a deep architecture-based tandem approach for unconstrained offiine handwriting recognition. In the proposed model, deep belief networks arc adopted to learn the compact representations of sequential data, while HMMs are applied for （sub-）word recognition. We evaluate the proposed model on two publicly available datasets, i.e., RIMES and IFN/ENIT, which are based on Latin and Arabic languages respectively, and one dataset collected by ourselves called Devanagari （all Indian script）. Extensive experiments show the advantage of the proposed model, especially over the MLP-HMMs taudem approaches.
基金Program for school-level Research in China University of Political Science and Law(16ZFQ82008).
文摘As a historic technology,handwriting identification,especially that of Chinese characters,plays an important role in judicial trials both in domestic and abroad.However,in recent decades,handwriting identification has feced a challenging situation;the scientificity of handwriting identification has been questioned.This article analyzes the reasons for questioning handwriting identification,focusing on the aspects of standards,methods,quantitative analysis of handwriting identification,and qualification,training,and assessment of document examiners.Besides,corresponding solutions for the above aspects and some other characteristics,such as systematic identification,cross-examination of expert opinions,and rule of corroboration,are proposed.
基金This research was funded by the Youth Scientist Program of the CUPL Science Research Project(2016),Grant No.16ZFQ82009.
文摘Handwriting expertise,as a form of forensic evidence,was once considered by most courts under the Anglo-American law system to be infallible,but this position was significantly challenged by the Daubert case(1993)and further by the President’s Council of Advisors on Science and Technology report published in 2016.In China,handwriting expertise has often been accepted as forensic evidence.However,this does not mean that there is no need to review the reliability of handwriting expertise.In this study,we analyze the current situation in China regarding the reliability of handwriting identification using cases from China’s judicial judgment database.We intend to identify the reasons for rejection of handwriting expertise,analyze the outcomes of applications for re-examination,and examine the court’s evaluation of different forensic opinions in relation to a given case.We also propose ways to strengthen the reliability of handwriting identification in China.
基金This work was funded by the Kazakhstan Centre for International Programmes through the Bolashaq International Scholarship Scheme.
文摘In 2017,the Republic of Kazakhstan began the phased transition of its alphabet from Cyrillic to Latin script.This transition has presented significant challenges to Kazakhstani document examiners,who have yet to develop appropriate methodologies for the analysis of handwriting samples written in the Kazakh language using Latin letters.This study aims to identify distinguishing macro and micro features of letters within Kazakh writing samples produced using the Latin alphabet and determine their frequencies of occurrence and discriminating power indices.Micro features were examined using the four most frequently appearing letters:“a”,“y”,“e”and“n”.A comparative analysis of tested Latin letters with those of a similar configuration in Cyrillic demonstrated differences in the number of distinguishing features,as well as in the frequency of occurrence and discriminating power indices of similar features.These results show that separate statistical bases should be used for Latin and Cyrillic letters when analysing handwriting samples based on the frequencies of occurrence of micro and macro writing features.
