Predicting the big five personality traits from hand-written text features through semi-supervised learning

Handwriting analysis is a systematic study of preserved graphic structures. Which are generated in the human brain and produced on paper in cursive or printed style. The style in which a text is written reflects an array of meta-information. Personality is a combination of an individual’s behavior, emotion, motivation, and thought-pattern characteristics. It has an impact on one’s life choices, well-being, health, and numerous other preferences. This study investigates the correlation between handwriting features and personality characteristics. The prediction of personality through handwriting analysis needs to investigate the style and structure of writing. This study extracts eleven features from handwriting samples using a graph-based writing representation approach. The Big Five model of personality traits is utilized to find the personality of the writer. To improve classification accuracy utilizes a Semi-supervised Generative Adversarial Network (SGAN). This network uses a small amount of labeled data and a larger amount of unlabeled data to train the classifier. The discriminator works as a multi-class classifier and is trained on labeled, unlabeled, and generator created data. The proposed system predicts 91.3% correct personality results by utilizing the writing features of 173 participants.

     

Arabic writer identification based on hybrid spectral–statistical measures

Many techniques have been reported for handwriting-based writer identification. None of these techniques assume that the written text is in Arabic. In this paper we present a new technique for feature extraction based on hybrid spectral–statistical measures (SSMs) of texture. We show its effectiveness compared with multiple-channel (Gabor) filters and the grey-level co-occurrence matrix (GLCM), which are well-known techniques yielding a high performance in writer identification in Roman handwriting. Texture features were extracted for wide range of frequency and orientation because of the nature of the spread of Arabic handwriting compared with Roman handwriting, and the most discriminant features were selected with a model for feature selection using hybrid support vector machine–genetic algorithm techniques. Four classification techniques were used: linear discriminant classifier (LDC), support vector machine (SVM), weighted Euclidean distance (WED), and the K nearest neighbours (K_NN) classifier. Experiments were performed using Arabic handwriting samples from 20 different people and very promising results of 90.0% correct identification were achieved.     

Text-independent writer identification and verification using textural and allographic features

The identification of a person on the basis of scanned images of handwriting is a useful biometric modality with application in forensic and historic document analysis and constitutes an exemplary study area within the research field of behavioral biometrics. We developed new and very effective techniques for automatic writer identification and verification that use probability distribution functions (PDFs) extracted from the handwriting images to characterize writer individuality. A defining property of our methods is that they are designed to be independent of the textual content of the handwritten samples. Our methods operate at two levels of analysis: the texture level and the character-shape (allograph) level. At the texture level, we use contour-based joint directional PDFs that encode orientation and curvature information to give an intimate characterization of individual handwriting style. In our analysis at the allograph level, the writer is considered to be characterized by a stochastic pattern generator of ink-trace fragments, or graphemes. The PDF of these simple shapes in a given handwriting sample is characteristic for the writer and is computed using a common shape codebook obtained by grapheme clustering. Combining multiple features (directional, grapheme, and run-length PDFs) yields increased writer identification and verification performance. The proposed methods are applicable to free-style handwriting (both cursive and isolated) and have practical feasibility, under the assumption that a few text lines of handwritten material are available in order to obtain reliable probability estimates     

Gender detection and identifying one's handwriting with handwriting analysis

Today, graphology is seen as an experimental field of science that is dedicated to suggest ideas about diseases, profession choices, mood and characteristics of a person by investigating his/her handwriting. Graphology is in cooperation with medicine, psychology, sociology or other disciplines that are based on observation. Graphology is used for staff recruitment in business, diagnoses in medicine, identification of criminals in forensics, choosing a profession in education, guidance and counseling and other practices at every level of social structure. It is quite interesting that the number of the scientific studies on graphology is limited around the world, that there are no specific institutions providing education of graphology and that institutions except for a few international corporations do not benefit from graphology at all.In terms of demographic properties, many statistical and mathematical analyses investigate similar and different variables. Especially, differences regarding gender have become subject to research. Therefore, detecting gender through handwriting can give pace to research in other disciplines. Moreover, the research can be useful in any field where gender detection is needed. This study fulfills two objectives. The first one is to find out whether a writer can identify his/her own handwriting. The second objective is to detect the gender of a writer of a text with the help of graphology and computer sciences. The impact of the study is reflected in the fact that findings can be used in fields where gender detection is needed, and that the detection is done with the help of expert and intelligent systems. At the end of the study, gender detection was performed for the individuals by making use of 133 attributes. Then, a decision tree and lists of rules were created with some algorithms. The purpose was to detect the gender of the person by making a character analysis of the handwriting with the help of decision tree formation methods in data mining. The analysis showed that it is possible to detect the gender of a person with the use of the specified attributes. The study reached a success level of 93.75% with ID3 algorithm.     

基于优化分割重建图像的文本无关笔迹鉴定

笔迹鉴定的主要过程首先是系统把手写的笔迹文字通过扫描仪输入计算机，然后对原始笔迹的图像进行预处理。在预处理阶段，本文提出了优化分割重建图像的归一化预处理方法，在参数提取阶段，本文采用多通道二维G2bro滤波器，通过计算4个方向每个方向4个频率来提取的笔迹特征。本文对10个人任意书写的笔迹进行实验，鉴别正确率得到较好的提高。     

Writer identification using directional ink-trace width measurements

As suggested by modern paleography, the width of ink traces is a powerful source of information for off-line writer identification, particularly if combined with its direction. Such measurements can be computed using simple, fast and accurate methods based on pixel contours, the combination of which forms a powerful feature for writer identification: the Quill feature. It is a probability distribution of the relation between the ink direction and the ink width. It was tested in writer identification experiments on two datasets of challenging medieval handwriting and two datasets of modern handwriting. The feature achieved a nearest-neighbor accuracy in the range of 63–95%, which even approaches the performance of two state-of-the-art features in contemporary-writer identification (Hinge and Fraglets). The feature is intuitive and explainable and its principle is supported by a model of trace production by a quill. It illustrates that ink width patterns are valuable. A slightly more complex variant of Quill, QuillHinge, scored 70–97% writer identification accuracy. The features are already being used by domain experts using a graphical interface.     

基于笔迹的身份鉴别

提出了一种鉴别笔迹的新方法.现有的笔迹识别方法大多需要进行分割或关联部分 的分析,都是与内容相关的方法.在新方法里,把手写笔迹作为一种纹理来看待,将笔迹鉴别 的问题转化为纹理识别来处理,这是一种与内容无关的方法.使用多通道二维Gabor滤波器 来提取这些纹理的特征,并使用加权欧氏距离分类器来完成匹配工作.在实验中,使用了17个 人的不同笔迹,取得了很好的结果.     

一种基于微结构特征的多文种文本无关笔迹鉴别方法

与字符识别一样, 计算机自动笔迹鉴别是一个涉及到不同文种的研究课题. 本文提出了一种基于网格窗口微结构特征的文本无关的笔迹鉴别方法, 能适用于各种不同文种的笔迹. 该方法对笔迹中局部细微结构的书写变化趋势进行描述, 并采用加权距离度量方法进行笔迹相似性度量. 利用该方法实现了文本无关的多文种笔迹检索系统, 并在实际汉字、英文、藏文和维吾尔文的笔迹库上进行了测试. 实验证明, 该方法是一种高效且适用性较广、限制性较少的笔迹鉴别方法.     

A statistical handwriting model for style-preserving and variable character synthesis

Synthesizing handwritten-style characters is an interesting issue in today’s handwriting analysis field. The purpose of this study is to artificially generate training data, foster a deep understanding of human handwriting, and promote the use of the handwritten-style computer fonts, in which the individuality or variety of the synthesized characters is considered important. Research considering such two properties together, however, is very rare. In this paper, a handwriting model is proposed to synthesize various handwritten characters while preserving the writer’s individuality from a limited number of training data, using a statistical approach. The proposed model is verified in single- and multiple-stroke characters, such as Arabic numbers, small English letters, and Japanese Kanji letters. Synthesized characters are evaluated in three ways. First, they are analyzed visually using the selected samples, and the relationship between the training and synthesized characters is explained. Second, the personalities and varieties of all the data are evaluated using a conventional writer verification method. Third, a questionnaire is developed and administered to evaluate the subjective responses of the users regarding the personal styles of the synthesized characters. The results prove that the proposed model stably synthesizes personalized characters by being invariant to the number of training data, whereas the variety increases gradually as the data increase.     

Writer identification of Chinese handwriting documents using hidden Markov tree model

Handwriting-based writer identification, a branch of biometrics, is an active research topic in pattern recognition. Since most existing methods and models aim to on-line and/or text-dependent writer identification, it is necessary to propose new methods for off-line, text-independent writer identification. At present, two-dimensional Gabor model is widely acknowledged as an effective and classic method for off-line, text-independent handwriting identification, while it still suffers from some inherent shortcomings, such as the excessive calculational cost. In this paper, we present a novel method based on hidden Markov tree (HMT) model in wavelet domain for off-line, text-independent writer identification of Chinese handwriting documents. Our experiments show this HMT method, compared with two-dimensional Gabor model, not only achieves better identification results but also greatly reduces the elapsed time on computation.     

Wavelet-based gender detection on off-line handwritten documents using probabilistic finite state automata

Detection of gender from handwriting of an individual presents an interesting research problem with applications in forensic document examination, writer identification and psychological studies. This paper presents an effective technique to predict the gender of an individual from off-line images of handwriting. The proposed technique relies on a global approach that considers writing images as textures. Each handwritten image is converted into a textur\ed image which is decomposed into a series of wavelet sub-bands at a number of levels. The wavelet sub-bands are then extended into data sequences. Each data sequence is quantized to produce a probabilistic finite state automata (PFSA) that generates feature vectors. These features are used to train two classifiers, artificial neural network and support vector machine to discriminate between male and female writings. The performance of the proposed system was evaluated on two databases, QUWI and MSHD, within a number of challenging experimental scenarios and realized classification rates of up to 80%. The experimental results show the superiority of the proposed technique over existing techniques in terms of classification rates.     

On the development of a computer-based handwriting assessment tool to objectively quantify handwriting proficiency in children

Standardized writing assessments such as the Minnesota Handwriting Assessment (MHA) can inform interventions for handwriting difficulties, which are prevalent among school-aged children. However, these tests usually involve the laborious task of subjectively rating the legibility of the written product, precluding their practical use in some clinical and educational settings. This study describes a portable computer-based handwriting assessment tool to objectively measure MHA quality scores and to detect handwriting difficulties in children. Several measures are proposed based on spatial, temporal, and grip force measurements obtained from a custom-built handwriting instrument. Thirty-five first and second grade students participated in the study, nine of whom exhibited handwriting difficulties. Students performed the MHA test and were subjectively scored based on speed and handwriting quality using five primitives: legibility, form, alignment, size, and space. Several spatial parameters are shown to correlate significantly (p < 0.001) with subjective scores obtained for alignment, size, space, and form. Grip force and temporal measures, in turn, serve as useful indicators of handwriting legibility and speed, respectively. Using only size and space parameters, promising discrimination between proficient and non-proficient handwriting can be achieved.     

Generating logistic chaotic sequence using geometric pattern to decompose and recombine the pixel values

The Secret Sharing Scheme plays a vital role in cryptography which allows to transmit the secret digital information (image, video, audio, handwriting, etc.,) over a communication channel. This cryptographic technique involves encrypting the secret images into noisy shares and transmitted. The transmitted image shares are reconstructed using simple logical computation. In this paper, we propose a secure (n, n)- Multi-Secret-Sharing (MSS) scheme using image scrambling algorithm which is based on the logistic chaotic sequence generated using the secret key which is retrieved from the geometric pattern named as spirograph which drawn by the users with their private values. Also, decomposition and recombination of image pixels which points to change the position and values of the pixels. The experimental results estimate that the standard metrics NPCR, UACI, Entropy, Coefficient Correlation values proves the rigidness of the implemented algorithm.

     

Why technology matters to writing: A cyberwriter’s tale

Technology does indeed matter to writing—and in significant ways. But how it matters can vary, depending on the particular technology, the habits and attitudes of the individual writer, and the context of learning and use. Here I employ a personal narrative (“a cyberwriter’s tale”) to track my development as a writer over time—from handwriting to typewriting to cyberwriting—and to show how each new writing technology influenced my practices and products. I argue finally for a cyborgian, posthumanist view of writing technologies. Such a view does not isolate the technological tool as an abstracted machine apart from human use, but insists on defining technology as use—as the human and machine working in concert (joined at the interface) and writing in a particular social, political, and rhetorical context.     

Improving handwriting based gender classification using ensemble classifiers

This paper presents a system to predict gender of individuals from offline handwriting samples. The technique relies on extracting a set of textural features from handwriting samples of male and female writers and training multiple classifiers to learn to discriminate between the two gender classes. The features include local binary patterns (LBP), histogram of oriented gradients (HOG), statistics computed from gray-level co-occurrence matrices (GLCM) and features extracted through segmentation-based fractal texture analysis (SFTA). For classification, we employ artificial neural networks (ANN), support vector machine (SVM), nearest neighbor classifier (NN), decision trees (DT) and random forests (RF). Classifiers are then combined using bagging, voting and stacking techniques to enhance the overall system performance. The realized classification rates are significantly better than those of the state-of-the-art systems on this problem validating the ideas put forward in this study.     

Online separation of handwriting from freehand drawing using extreme learning machines

Online separation between handwriting and freehand drawing is still an active research area in the field of sketch-based interfaces. In the last years, most approaches in this area have been focused on the use of statistical separation methods, which have achieved significant results in terms of performance. More recently, Machine Learning (ML) techniques have proven to be even more effective by treating the separation problem like a classification task. Despite this, also in the use of these techniques several aspects can be still considered open problems, including: 1) the trade-off between separation performance and training time; 2) the separation of handwriting from different types of freehand drawings. To address the just reported drawbacks, in this paper a novel separation algorithm based on a set of original features and an Extreme Learning Machine (ELM) is proposed. Extensive experiments on a wide range of sketched schemes (i.e., text and graphical symbols), more numerous than those usually tested in any key work of the current literature, have highlighted the effectiveness of the proposed approach. Finally, measurements on accuracy and speed of computation, during both training and testing stages, have shown that the ELM can be considered, in this research area, the better choice even if compared with other popular ML techniques.

     

Predicting user demographics from music listening information

Online activities such as social networking, online shopping, and consuming multi-media create digital traces, which are often analyzed and used to improve user experience and increase revenue, e. g., through better-fitting recommendations and more targeted marketing. Analyses of digital traces typically aim to find user traits such as age, gender, and nationality to derive common preferences. We investigate to which extent the music listening habits of users of the social music platform Last.fm can be used to predict their age, gender, and nationality. We propose a feature modeling approach building on Term Frequency-Inverse Document Frequency (TF-IDF) for artist listening information and artist tags combined with additionally extracted features. We show that we can substantially outperform a baseline majority voting approach and can compete with existing approaches. Further, regarding prediction accuracy vs. available listening data we show that even one single listening event per user is enough to outperform the baseline in all prediction tasks. We also compare the performance of our algorithm for different user groups and discuss possible prediction errors and how to mitigate them. We conclude that personal information can be derived from music listening information, which indeed can help better tailoring recommendations, as we illustrate with the use case of a music recommender system that can directly utilize the user attributes predicted by our algorithm to increase the quality of it’s recommendations.

     

NN-based Prediction Interval for Nonlinear Processes Controller

Neural networks (NNs) are extensively used in modelling, optimization, and control of nonlinear plants. NN-based inverse type point prediction models are commonly used for nonlinear process control. However, prediction errors (root mean square error (RMSE), mean absolute percentage error (MAPE) etc.) significantly increase in the presence of disturbances and uncertainties. In contrast to point forecast, prediction interval (PI)-based forecast bears extra information such as the prediction accuracy. The PI provides tighter upper and lower bounds with considering uncertainties due to the model mismatch and time dependent or time independent noises for a given confidence level. The use of PIs in the NN controller (NNC) as additional inputs can improve the controller performance. In the present work, the PIs are utilized in control applications, in particular PIs are integrated in the NN internal model-based control framework. A PI-based model that developed using lower upper bound estimation method (LUBE) is used as an online estimator of PIs for the proposed PI-based controller (PIC). PIs along with other inputs for a traditional NN are used to train the PIC to predict the control signal. The proposed controller is tested for two case studies. These include, a chemical reactor, which is a continuous stirred tank reactor (case 1) and a numerical nonlinear plant model (case 2). Simulation results reveal that the tracking performance of the proposed controller is superior to the traditional NNC in terms of setpoint tracking and disturbance rejections. More precisely, 36% and 15% improvements can be achieved using the proposed PIC over the NNC in terms of IAE for case 1 and case 2, respectively for setpoint tracking with step changes.

     

Financial distress prediction based on OR-CBR in the principle of k-nearest neighbors

Financial distress prediction including bankruptcy prediction has called broad attention since 1960s. Various techniques have been employed in this area, ranging from statistical ones such as multiple discriminate analysis (MDA), Logit, etc. to machine learning ones like neural networks (NN), support vector machine (SVM), etc. Case-based reasoning (CBR), which is one of the key methodologies for problem-solving, has not won enough focus in financial distress prediction since 1996. In this study, outranking relations (OR), including strict difference, weak difference, and indifference, between cases on each feature are introduced to build up a new feature-based similarity measure mechanism in the principle of k-nearest neighbors. It is different from traditional distance-based similarity mechanisms and those based on NN, fuzzy set theory, decision tree (DT), etc. Accuracy of the CBR prediction method based on OR, which is called as OR-CBR, is determined directly by such four types of parameters as difference parameter, indifference parameter, veto parameter, and neighbor parameter. It is described in detail that what the model of OR-CBR is from various aspects such as its developed background, formalization of the specific model, and implementation of corresponding algorithm. With three year’s real-world data from Chinese listed companies, experimental results indicate that OR-CBR outperforms MDA, Logit, NN, SVM, DT, Basic CBR, and Grey CBR in financial distress prediction, under the assessment of leave-one-out cross-validation and the process of Max normalization. It means that OR-CBR may be a preferred model dealing with financial distress prediction in China.     

基于手写体的身份识别

以前许多文章曾介绍过一些基于手写体的书写人身份识别技术，其中多数都假设所写的文本是固定的。本文中，我们试图通过一种自动的不依赖文本的书写人识别听新颖算法，来消除这种假设，假定不同的人手写体存在明显的区别，我们采用一种综合方法，它基于纹理分析，每个人的手写体都被看成一种不同的纹理。原则上，我们可以采用任意一种标准的纹理识别算法（例如：多通道伽柏滤波器方法）。在对40名书写人的1000份测试文档的分类中，测试结果非常令人满意，识别率最高达到了96%。