Hand Drawing Research Articles

A Computer Vision System for an Automated Scoring of a Hand-drawn Geometric Figure

Visual Reproduction is a condition to measure Visual Spatial Memory as one of the cognitive domains commonly used to measure visuo-spatial memory. Geometric figures serve as stimulus material, and probands have to reproduce the figures from memory through a hand drawing. The scoring of the drawing has subjective elements. This study aims to evaluate the scoring criteria for the Figural Reproduction Test (FRT), part of the Indonesian Neuropsychological Test Battery, and to develop and evaluate an automated scoring system based on computer vision technology (FRT-CVAS). Scoring evaluation conducted by Cohen Kappa analysis, accuracy, sensitivity, and specificity. The analyzes of the three criteria of the manual confirmed a subjective element in the scoring of the shape of triangles by a moderate (0.74) inter-rater agreement; this agreement could be improved to 0.84 by a slight modification of its criteria. FRT-CVAS, based on computer vision’s identification of the different elements of the hand drawing, was developed and trained using 290 drawings. The system was additionally tested by comparing its scoring with the scoring of two independent raters on 120 drawings from a second data set. FRT-CVAS recognized all elements, and its comparison between human raters showed a high accuracy and sensitivity (minimally 0.91), while the specificity was 0.80 for one of the three criteria. FRT-CVAS offers a highly standardized, consistent, precise, and objective output from the first card in the FRT. This approach is advantageous to data-hungry alternatives such as deep learning when applied to the automated scoring of hand drawings with relatively little data available for training.

Canopy coverage of wheat measured with high accuracy using the HSV colour model and relative depth estimation model, MiDaS

ABSTRACT Canopy coverage calculated from 2D images effectively estimates several parameters, such as dry weight above the ground, stem number, and risk of lodging, without destroying the crops. This study aimed to clarify how to accurately calculate canopy coverage, regardless of environmental conditions, using various colour models and a relative depth estimation model, MiDaS. For wheat cultivated in 2022/23, the canopy coverage at the initiation of the stem elongation stage (CC30), calculated using only the HSV colour model, was overestimated because of the presence of weeds. However, when the heatmap generated using MiDaS was combined with the results extracted from the HSV colour model, it showed a CC30 similar to the accurate CC30 calculated by hand drawing, regardless of the wheat variety, sowing pattern, and location of the image taken, with an RMSE as low as 3.48. The CC30 calculated by combining the HSV colour model and MiDaS was significantly positively correlated with the number of stems, dry weight above ground, and nitrogen content above ground at the initiation of the stem elongation stage of ‘Sanukinoyume 2009’ grown in 2021/22 and 2022/23 (n = 72, r > 0.798). The combination of the HSV colour model and MiDaS effectively and accurately calculates canopy coverage regardless of environmental conditions and can be used for growth diagnosis and variable nitrogen fertiliser application after the initiation of the stem elongation stage.

Parkinson’s disease detection through hand drawings using Alexnet model

Parkinson’s disease detection through hand drawings using Alexnet model

A New Generic Record of Phyllanthaceae (Phyllanthus L.): Addition to the Alien Invasive Vascular Flora of Türkiye

The genus Phyllanthus L. and its species P. tenellus Roxb. (Phyllanthaceae) are reported as new genus and species for the vascular flora of Türkiye. A new Turkish name of the species is given. This new invasive greenhouse weed (in Türkiye) are provided with both photographic and technical hand drawing illustrations to facilitate future identification. A description of the genus and species are also provided in detail. Specimens of the species were collected from both research and application greenhouse of Faculty of Forestry in Karadeniz Technical University (KTÜ) and Of Forest Nursery Directorate greenhouses.

Influencing Factors of Spatial Ability for Architecture and Interior Design Students: A Fuzzy DEMATEL and Interpretive Structural Model

Spatial ability is not just a skill but a crucial element for architecture and interior design students, significantly impacting their proficiency in tasks involving 2D drawings, 3D components, and artistic expression. Despite extensive research in this area, a gap remains in the understanding of how to effectively cultivate spatial ability through educational interventions. This study, with its unique approach of identifying key influencing factors and their interrelationships within the fuzzy decision-making laboratory analysis method (Fuzzy-DEMATEL) and the interpretative structural model (ISM), fills this gap. The method visualizes cause-and-effect relationships within a structural model and captures the interdependencies between influencing factors. In a collaborative effort between nine universities in 2023–2024, 17 experts selected through purposeful sampling contributed to the development of a comprehensive list of potential influencing factors. After refinement through filtering, comparison with the existing literature, and expert consensus, seven influencing factors of spatial ability for architecture and interior design students from personal traits and STEAM disciplines were identified, which are sketching and hand drawing skills, mathematical skills, video game practice, descriptive geometry skills, augmented reality practice, spatial talk, and gesturing while talking. Sketching and hand drawing skills, mathematical skills, and video game practice come under cause factors of spatial ability, whereas the rest are effect factors. Proceeding with ISM analysis revealed that sketching and hand drawing skills and mathematical skills are located in the input layer and have a continuous impact on spatial ability. Descriptive geometry skills lie in the transition layer, which are considered as deep influencing factors, the rest of the factors lie in the effect layer. This study delves into the theoretical and practical implications of these findings, offering valuable insights for educational policy and practice.

Cognitive activity analysis of Parkinson's patients using artificial intelligence techniques.

The development of modern Artificial Intelligence (AI) based models for the early diagnosis of Parkinson's disease (PD) has been gaining deep attention by researchers recently. In particular, the use of different types of datasets (voice, hand movements, gait, etc.) increases the variety of up-to-date models. Movement disorders and tremors are also among the most prominent symptoms of PD. The usage of drawings in the detection of PD can be a crucial decision-support approach that doctors can benefit from. A dataset was created by asking 40 PD and 40 Healthy Controls (HC) to draw spirals with and without templates using a special tablet. The patient-healthy distinction was achieved by classifying drawings of individuals using Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB) algorithms. Prior to classification, the data were normalized by applying the min-max normalization method. Moreover, Leave-One-Subject-Out (LOSO) Cross-Validation (CV) approach was utilized to eliminate possible overfitting scenarios. To further improve the performances of classifiers, Principal Component Analysis (PCA) dimension reduction technique were also applied to the raw data and the results were compared accordingly. The highest accuracy among machine learning based classifiers was obtained as 90% with SVM classifier using non-template drawings with PCA application. The model can be used as a pre-evaluation system in the clinic as a non-invasive method that also minimizes environmental and educational level differences by using simple hand gestures such as hand drawing, writing numbers, words, and syllables. As a result of our study, preliminary preparation has been made so that hand drawing analysis can be used as an auxiliary system that can save time for health professionals. We plan to work on more comprehensive data in the future.

ChemReco: automated recognition of hand-drawn carbon–hydrogen–oxygen structures using deep learning

Chemical molecular structures are a direct and convenient means of expressing chemical knowledge, playing a vital role in academic communication. In chemistry, hand drawing is a common task for students and researchers. If we can convert hand-drawn chemical molecular structures into machine-readable formats, like SMILES encoding, computers can efficiently process and analyze these structures, significantly enhancing the efficiency of chemical research. Furthermore, with the progress of educational technology, automated grading is gaining popularity. When machines automatically recognize chemical molecular structures and assess the correctness of the drawings, it offers great convenience to teachers. We created ChemReco, a tool designed to identify chemical molecular structures involving three atoms: C, H, and O, providing convenience for chemical researchers. Currently, there are limited studies on hand-drawn chemical molecular structures. Therefore, the primary focus of this paper is constructing datasets. We propose a synthetic image method to rapidly generate images resembling hand-drawn chemical molecular structures, enhancing dataset acquisition efficiency. Regarding model selection, the hand-drawn chemical molecule structural recognition model developed in this article achieves a final recognition accuracy of 96.90%. This model employs the encoder-decoder architecture of EfficientNet + Transformer, demonstrating superior performance compared to other encoder-decoder combinations.

Architectural drawing at Gdańsk University of Technology from 1904 to 2024

The article analyzed the programs and teaching methods at the Faculty of Architecture of the Gdańsk University of Technology in terms of hand drawing as the basic means of architectural teaching in the 20th century. Contemporary changes in this area are also presented. The Gdańsk University of Technology (Technische Hochschule Danzig) was established in 1904 as a university with German as the language of instruction. The Faculty of Archi- tecture existed there from the beginning. In 1945, the former was transformed into the Polish Gdańsk University of Technology (Politechnika Gdańska). After 1945, the Faculty of Architecture of the Gdańsk University of Technology continued mainly the traditions of the two faculties existing before World War II at the Lviv University of Technology and the Warsaw University of Technology, and the Faculty of Fine Arts at the University of Vilnius. The most important was the tradition of the Lviv school, from which the curricu- lum was transferred almost unchanged after 1945 and from which most of the teachers at the Gdańsk Faculty came.

Integration effect of artificial intelligence and traditional animation creation technology

Abstract Despite the advancements in modern computer hardware and software, the creation of digital animation still demands a substantial investment of both manpower and time. This article aimed to explore how artificial intelligence (AI) technology can be combined with traditional animation creation techniques to achieve better integration effects. By combining intelligent character animation generation with hand drawing, a generative adversarial network was used to achieve high-quality animation generation. The generator generated realistic animations, and the discriminator measured the authenticity of the animations by comparing the differences between the generator-generated animations and the real animations, which was used for automated character animation generation. This can greatly reduce the cost and time of digital animation creation, improve the quality of digital animation, and provide more innovation for the application of traditional animation technology. The average number of audience attracted by characters through blended creation was 122.2% more than that of characters through traditional animation creation and 32.6% more than that of characters through AI creation. This not only helps animation producers complete animation production more quickly, but also enhances the creativity and artistic value of animation.

Observations on the Names, Descriptions, and Drawings of Musical Instruments, Named “Ekhlegch”, “Khuraagch” and “Uelegch”

Abstract This article was written as a contribution to the study of Mongolian musicology, including court musicology. The musical instruments made of wood, named “Ekhlegch”, “Khuraagch” and “Uelegch” were a part of the court musical orchestra of the Great Yuan State of Mongolia although they are no longer used, their purpose is not always clear, the names, descriptions, and drawings of them have been recorded in some old sources and works of musicologists. It is believed that studying these instruments and clarifying their functions will contribute to the further development of Mongolian musical studies and the structure and composition of the orchestra. Also, it would be another interesting study to compare the hand drawings of musical instruments, which were recorded in old sources as part of the ancient Mongolian court orchestra, but have not been preserved today.

A 3D Descriptive Geometry Problem-Solving Methodology Using CAD and Orthographic Projection

In solving descriptive geometry (DG) problems, board (i.e., hand drawing) methods are frequently used, despite this discipline is still very important to enhance spatial vision. These methods are very different from CAD tools which are used in the field of design. CAD facilitates the realization of geometric constructions and transformations (i.e., rotation, translation, copying, scaling, alignment, and symmetry, among others) are performed analytically. For this reason, a 3D DG problem-solving methodology using CAD and orthographic projection (CADOP) is introduced. Once the principles of DG, orthographic projection, and CAD fundamentals and tools are described, CADOP is applied to obtain (i) orthogonal views; (ii) principal lines of a plane; (iii) true-size view of a plane; (iv) parallelism, perpendicularity, and distance, and (v) angles. Considering the user coordinate system in CADOP allows one to place the horizontal plane in the suitable position to solve DG problems directly in one step. In the traditional methods, the use of auxiliary views must be carried out in several steps instead. The dynamic management of the 3D view of the scene is facilitated in CADOP, improving its understanding, and achieving the precision inherent in analytical calculations.

Hand drawing-based daylight analysis using deep learning and augmented reality

The utilization of artificial intelligence (AI) in daylight analysis has recently experienced an increase. Its capabilities have been demonstrated through different studies. While using AI for daytime simulation can reduce time, it also presents significant challenges. Whether we utilize the conventional approach of simulating daylight or employ artificial intelligence, the process of going from hand drawing to obtaining the daylight analysis result involves intricate procedures associated with modeling, programming, and integrating AI models. This paper introduces an efficient approach to rapidly predict daylight performance directly from the user's hand-drawn sketches. The core idea is to leverage a deep learning model for predicting directly from hand drawing and augmented reality (AR) to seamlessly display results in an AR environment, overlaying them on the original drawings. This method not only increases user engagement and accessibility but also eliminates the dependency on traditional daylight simulation software and workflow. Artificial neural network (ANN) was exploited using data derived from 3D building models and Useful Daylight Illuminance simulations created by Rhino3D and Honeybee. The color code method, various imaging, and data processing techniques were implemented to ensure compatibility between the hand-drawn input and the simulation model. Unity3D is used as the development platform with the AR engine Vuforia. Notably, the ANN model yields promising results, with R2 ranging from 0.872 to 0.985. This is achieved by efficiently capturing and converting grid-based hand-drawn images into suitable input for the ANN. The obtained results demonstrate the feasibility and emphasize the concept's practicality.

Cosine deep convolutional neural network for Parkinson’s disease detection and severity level classification using hand drawing spiral image in IoT platform

In recent years, the Internet of Things (IoT) is an emerging technology that plays a vital role in the early detection and diagnosis of Parkinson’s disease-affected patients using various healthcare monitoring systems at any time. The utilization of IoT in real-time by monitoring Parkinson’s disease-affected patients in-home care to enhance the quality of life. Thus in this research, a hybrid deep learning classifier Cosine Deep Convolutional Neural network (CosineDCNN) is developed for early detection and classification of Parkinson’s disease. Initially, routing is performed using the designed Sine Cosine Geese Migration Optimization (SCGA) to determine the optimal routing path to transmit the hand-drawing spiral images to the base station. At the base station, the hand-drawing spiral images are pre-processed, and augmented, and the features are extracted from them for Parkinson’s disease detection and severity classification using CosineDCNN. The results obtained from the experiment proved that the CosineDCNN achieved superior performance with 89.98 % accuracy, 87.41 % Positive Predictive Value (PPV), 89.77 % True Negative Rate (TNR), 87.82 % Negative Predictive Value (NPV), and 89.84 % True Positive Rate (TPR) as compared with other techniques. Moreover, the routing performance of the SCGA model is also computed, which recorded a minimum of 0.283mS delay and a maximum of 0.507 J energy.

Highly conductive, super-stretchable and stable stretchable conductor with CNT and liquid metal alternating layered structure

Liquid metal (LM) composites, known for their excellent conductivity and fluidity, hold great promise in the fields of printed electronics and wearable technology. However, their high surface tension and fluidity can lead to circuit instability, such as leakage during deformation, resulting in short circuits. Therefore, additional methods are necessary to enhance the stability of LM within composite material systems. In this paper, Carbon nanotube (CNT) was introduced as a transition layer to enhance the adhesion of gallium-indium-copper-alloy ink (GCLA) on different elastic substrates and to obtain interface fusion conductive layers. The modified GCLA conductive fibers achieved high conductivity (conductivity up to 3.13 × 105 S/m), ultra-stretchability (1580 % of ultra-large deformation, the resistance changes less than 3 Ω), and ultra-stability (within 200 % of large deformation, the change of resistance less than 1 Ω). In addition, we also demonstrated the operability of GCLA in forming patterns on hydrophilic and hydrophobic substrate surfaces and obtained conductive patterns through printing, transfer printing, and hand drawing, making it a promising candidate for flexible electronics.

Gesture Controlled Virtual Artboard

Abstract: Hand Gesture Recognition has brought a brand new era to Artificial Intelligence. Gesture recognition is technology that interprets hand movements as commands. Gesture Controlled Virtual Artboard Project is an innovative and dynamic digital artboard. Use of this artboard can break various education barrier to provide students with fun and creative way of learning and hence It revolutionize the way of traditional teaching. Physically challenged or aged people find it difficult to identify and press the exact key on keyboard. Existing system allows user for free hand drawing with 3 different colors only. Overcoming all this difficulty stands as the ultimate goal for our proposed system. This system works on motion sensing technology which make use of OpenCV module & Mediapipe library to explicate results based on gestures of hand. Real time data is collected through webcam and this python application uses Mediapipe library to track hand gestures. Hence, this system allows user to navigate the fingers in mid air and as per the finger gestures they will draw different shapes or free hand drawing in various colors and also can erase.This Project also enables users to conduct PowerPoint presentations using gestures.

Watching videos of a drawing hand improves students' understanding of the normal probability distribution.

Understanding normal probability distributions is a crucial objective in mathematics and statistics education. Drawing upon cognitive psychology research, this study explores the use of drawings and visualizations as effective scaffolds to enhance students' comprehension. Although much research has documented the helpfulness of drawing as a research tool to reveal students' knowledge states, its direct utility in advancing higher-order cognitive processes remains understudied. In Study 1, qualitative methods were utilized to identify common misunderstandings among students regarding canonical depictions of the normal probability distribution. Building on these insights, Study 2 experimentally compared three instructional videos (static slides, dynamic drawing, and dynamic drawings done by a visible hand). The hand drawing video led to better learning than the other versions. Study 3 examined whether the benefits from observing a hand drawing could be reproduced by a dynamic cursor moving around otherwise static slides (without the presence of a hand). Results showed no significant learning difference between observing a hand drawing and a moving cursor, both outperforming a control. This research links the cognitive process of drawing with its educational role and provides insights into its potential to enhance memory, cognition, and inform instructional methods.

Within the Space of Drawing: Lines and the Locus of Creation in Architectural Design

This paper considers the practice of drawing lines in the context of architectural design. The core argument is that drawing lines generates the conditions for creative thought. Moreover, this initial claim is discussed in the context of the creative process in architectural design, as lines play an indispensable role in the locus of creation. First, the so-called “representational paradigm” about hand drawing is critically discussed, leading to the exposition of a new philosophical account regarding drawing. This new position consists of three theses: (I) it regards the drawing surface as a topos or “space of drawing”; (II) it regards drawings as situated figurations; (III) and it regards lines as processes. Jointly, these three theses form the “performative paradigm”, casting each aspect of the drawing process in terms of an unfolding dynamic in which inhabitative imagination and aesthetic sensibility play decisive roles. Lastly, these conclusions are formalized in a design model.

Modeling and diagnosis Parkinson disease by using hand drawing: deep learning model

<abstract> <p>Patients with Parkinson's disease (PD) often manifest motor dysfunction symptoms, including tremors and stiffness. The presence of these symptoms may significantly impact the handwriting and sketching abilities of individuals during the initial phases of the condition. Currently, the diagnosis of PD depends on several clinical investigations conducted inside a hospital setting. One potential approach for facilitating the early identification of PD within home settings involves the use of hand-written drawings inside an automated PD detection system for recognition purposes. In this study, the PD Spiral Drawings public dataset was used for the investigation and diagnosis of PD. The experiments were conducted alongside a comparative analysis using 204 spiral and wave PD drawings. This study contributes by conducting deep learning models, namely DenseNet201 and VGG16, to detect PD. The empirical findings indicate that the DenseNet201 model attained a classification accuracy of 94% when trained on spiral drawing images. Moreover, the model exhibited a receiver operating characteristic (ROC) value of 99%. When comparing the performance of the VGG16 model, it was observed that it attained a better accuracy of 90% and exhibited a ROC value of 98% when trained on wave images. The comparative findings indicate that the outcomes of the proposed PD system are superior to existing PD systems using the same dataset. The proposed system is a very promising technological approach that has the potential to aid physicians in delivering objective and dependable diagnoses of diseases. This is achieved by leveraging important and distinctive characteristics extracted from spiral and wave drawings associated with PD.</p> </abstract>

Perancangan Presentasi Produk Arsitektur Menggunakan Teknologi Virtual Reality

Presentation in architecture has developed over time. From initially using hand drawings, he then switched to using visuals created by computers. The more technology develops, the more interesting the presentations produced. The results of the presentation also become more realistic so that they are closer to the original design object. Technological developments are increasingly advanced and the use of Virtual Reality technology is widely used in almost all fields, including in the field of architecture. In Architecture, the use of Virtual Reality as a presentation tool has been widely used. However, the method used requires quite a large amount of money and is still too complicated to apply. However, currently Virtual Reality can be done simply and can be applied generally and by architectural practitioners.

Teaching Engineering Graphics through Digital Presentations for Engineering Students

Abstract: All new students at the University of Prishtina “Hasan Prishtina”, Faculty of Mechanical Engineering, take an Engineering Graphics course in the first semester. The subject is divided into two sections: Technical Drawing and Descriptive Geometry. To teach lectures, professors use didactic equipment such as LCD projectors and drawing on the table. Assistants demonstrate exercises by drawing mechanical parts in the table. In 2014, teachers began using digital presentations with animations in their lectures. They contain developed presentations using PowerPoint software with stepby- step animations of mechanical parts. The paper examines the effectiveness of this methodology from both the student and teacher perspectives. The study is based on a survey of students, teachers’ experiences, and a comparison of exam results. The analysis discusses the methodology's effectiveness, presentation quality, knowledge gained, ease of learning, impact on exam pass rate, and other benefits.The following are some key findings from this paper: students accept and favor this teaching methodology; it is more effective than hand drawing; it has a significant impact on exam pass rates; and it is simple to understand the lectures. Teachers like this methodology because it allows for step-by-step explanation with animations; repeating options; presentations are created only once, easy corrections, developing many examples of mechanical parts with less effort, and students can easily access them. The paper contributes to a new teaching methodology for students in their first year of Engineering studies, Bachelor level, in the subject of Engineering Graphics. Keywords: Engineering graphics, Teaching methodology, Digital presentations, Animations, Higher education, Teaching survey.