An Enhanced Hybrid Model Based on CNN and BiLSTM for Identifying Individuals via Handwriting Analysis

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.

Person-Dependent Handwriting Verification for Special Education Using DeepLearning

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.

Handwriting Classification Based on Support Vector Machine with Cross Validation

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.

Neural and Kinematic Metrics of Handwriting in Neurotypical Adults

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.

The Effect of a Visual Memory Training Program on Chinese Handwriting Performance of Primary School Students with Dyslexia in Hong Kong

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.

Research of handwriting detecting system for space pen

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.

Comparison of ANN and SVM to Identify Children Handwriting Difficulties

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.

Extraction of Arabic Handwriting Fields by Forms Matching

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%.

Automated Handwriting Recognition and Speech Synthesizer for Indigenous Language Processing

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.

Perceptual Visuo-Motor Skills and Handwriting Production of Children With Learning Disabilities

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.

Boosting the Expense and Performance of Ann/Hmm Approch for on-line Handwriting Recognition

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.

MNIST Handwritten Digit Classification Based on Convolutional Neural Network with Hyperparameter Optimization

Accurate handwriting recognition has been a challenging computer vision problem,because static feature analysis of the text pictures is often inade-quate to account for high variance in handwriting styles across people and poor image quality of the handwritten text.Recently,by introducing machine learning,especially convolutional neural networks(CNNs),the recognition accuracy of various handwriting patterns is steadily improved.In this paper,a deep CNN model is developed to further improve the recognition rate of the MNIST hand-written digit dataset with a fast-converging rate in training.The proposed model comes with a multi-layer deep arrange structure,including 3 convolution and acti-vation layers for feature extraction and 2 fully connected layers(i.e.,dense layers)for classification.The model’s hyperparameters,such as the batch sizes,kernel sizes,batch normalization,activation function,and learning rate are optimized to enhance the recognition performance.The average classification accuracy of the proposed methodology is found to reach 99.82%on the training dataset and 99.40%on the testing dataset,making it a nearly error-free system for MNIST recognition.

智能书写分析与识别平台用于6~12岁儿童书写能力评估的研究报告

目的通过人工智能分析书写能力评估数据,准确评估小学生的书写质量和能力,为精准筛查和评估书写困难者提供数据支撑。方法采用横断面研究,使用SHARP书写智能分析平台对福建省福州市闽江师范高等专科学校附属实验小学一到六年级(总计1085名)学生进行书写能力评估。评估指标包括书写速度、书写控制、书写统合、纸上时间比率、书写成果准确性和书写总体表现。结果女生在书写控制、书写统合、纸上时间比率、书写成果准确性和书写总体表现上优于男生(P<0.05)。右利手在书写统合、纸上时间比率和书写总体表现3项指标上优于左利手(P<0.05)。小学生书写能力发展趋势:①在书写速度上,一到六年级总体呈逐步加快的趋势,随着年级的增长,一到四年级相邻年级之间书写速度逐步加快(P<0.05),五到六年级之间书写速度逐步加快(P<0.05),但四到五年级之间书写速度的增长差异无统计学意义(P>0.05);②在书写控制能力上,一到六年级总体呈逐步发展的趋势,三年级书写控制能力表现优于二年级(P<0.05),五年级书写控制能力表现优于四年级(P<0.05),其余相邻年级之间差异无统计学意义(P>0.05);③在书写统合能力和纸上时间比率方面,随着年级的增长,一到四年级相邻年级之间高年级学生的发展优于低年级(P<0.05),四到五、五到六年级之间差异无统计学意义(P>0.05);④在书写成果准确性上,二年级优于一年级(P<0.05),四年级优于三年级(P<0.05),其余相邻年级之间差异无统计学意义(P>0.05);⑤在书写总体表现上,二年级表现优于一年级(P<0.05),其余相邻年级之间差异均无统计学意义(P>0.05)。书写控制、书写统合和书写成果准确性与书写总体表现之间呈显著负相关(r>-0.5),纸上时间比率与书写总体表现之间呈高度正相关(r=0.575),纸上时间比率与书写统合之间呈高度负相关(r=-0.999)。书写成果准确性仅能解释书写总体表现的39.1%变化原因,书写成果准确性、纸上时间比率、书写控制和书写统合能力共同解释书写总体表现的71.8%变化原因。结论在小学阶段,女生的书写能力普遍优于男生,但性别和优势手仍无法预测书写的总体表现。书写能力各测量指标随着年级的增高表现趋于成熟,书写速度大约在四到五年级时进入一个短暂的平台期后继续发展;书写控制能力在二到三年级、四到五年级时发展更为迅速,书写统合能力和在纸上时间比率约在四年级后进入相对稳定的平台期。书写成果准确性不适合单独作为书写总体表现的预测因素;书写控制、书写统合和纸上时间比率对书写总体表现具有较强的影响,可以综合作为书写总体表现的预测因素。

融合笔迹特征的可信签字图章生成及验证方法

随着电子通信与互联网技术的迅速发展,文件流转和处理正在逐步转向数字化,电子文件的签署方式呈现出更为便捷灵活与多样化的趋势,如在线签字采集、远程签字确认以及电子签名认证等。与此同时,文件的签署和验证过程在真实性、完整性等方面面临着诸多严峻挑战。不法分子以低成本手段截取、复制和伪造签字图像冒名签署文件、篡改和伪造签名文件等案例层出不穷,电子签章系统的应用过程面临成本较高、部署受限、普适性缺乏,以及真实性和一致性校验复杂等方面的困境。为了应对这些潜在的风险和挑战,实现个人文件签署流转过程中的可靠验证,提出了一种融合笔迹特征的可信签字图章生成及验证方法,该方法主要通过可信身份认证平台的人脸识别和身份信息匹配功能,融合签字笔迹特征,对签字笔迹特征唯一绑定并对签名者身份可靠认证,并基于此生成融合笔迹特征、签署文件验证链接和数字签名二维码的签字图章。分析表明:所提方法的签字图章不仅具备身份验证的功能,而且能够实现对文件签署真实性的辨别,二维码中签署文件验证链接可通过在线渠道直接验证签名人的身份、笔迹以及签署文件的真实性和一致性,这为纸质文件验证提供了更为便捷的途径,所提方法在电子文件流转处理和电子与纸质文件真实性验证领域具有广泛的应用前景。

网络手写的赛博文字:“具身-物性”与后人类未来的降维存在

网络手写是目前年轻人比较热衷的文字书写方式,通过书写媒介和网络社交平台,能够方便地将手写文字的个性笔迹,与名家书法或基准字库的视觉美学融为一体,生成视觉“奇异性”的赛博文字、数字书法或文字数码物,带给网民参与的书写乐趣、趣缘交往和文创商机。文章主要运用许煜分析一般“数码物”时引自于西蒙栋思考一般“技术物”时所采用的“数量级”概念和方法,结合“一起练字”网络手写平台的相关个案和经验材料,从书写文字与书写者身体的媒介关系及其演化历史出发,尝试建构融合“个体之物”与“环境之物”两种不同数量级之物的“具身-物性”阐释框架,从而较为细致地阐明网络手写文字如何以“模拟拼贴”和“数字生成”构成文字数码物的两种创作模式;同时分析网络手写平台如何由于未能有效解决网民手写文字的网络“可见性”所导致的社交贫困问题,提出可能的“争胜性”策略。文章将网络手写文字的探讨,定位在更普遍的人机交互和网络社交领域,强调正是作为模拟媒介的人类肉身和作为数字媒介的计算机网络及其算法之间不对等的数量级转导,实现了赛博文字及其网络交往,并将相关讨论扩展到更广泛的后人类未来可能以数字降维和图像交流为存在方式的媒介哲学。