Stroke Width Research Articles

Methodology for eliminating plain regions from captured images

Finding relevant content and extracting information from images is highly significant. Still, it may be challenging to do so because of changes within the textual contents, such as typefaces, size, line orientation, sophisticated backgrounds in images, and non-uniform illuminations. Despite these challenges, extracting content from captured images is still very important. Proficient textual content image recognition abilities extract text from the images to get over these issues. Despite the availability of several optical character recognition (OCR) techniques, this issue has yet to be resolved. Captured images with text are a rich source of information that should be presented so that viewers may make informed decisions. Because of this, it has become a complicated process to extract the text from an image because the text might be of poor quality, has a variety of fonts and styles, and occasionally have a complicated backdrop, among other things. Several approaches have been tried. However, finding a solution remains challenging. The maximally stable external regions (MSER) approach is developed to identify the text region in a picture. MSER is utilized to elevate the plain regions outside the text and non-text areas using geometric features and stroke width variation qualities.

Eliminating sticking effects among fine strokes in micropatterning complex Chinese characters

In this work, complex Chinese micropatterns are experimentally inscribed in AgInSbTe thin films through laser writing technique. The overlapping inscription strategy is introduced to reduce the size of complex Chinese micropatterns, and the erosion algorithm is used to eliminate sticking effects among fine strokes in complex Chinese micropatterns. The results show that the size of complex Chinese micropatterns can be reduced to around 15.41µm×15.41µm. The width of the finest stroke is only 0.45 µm. This method can achieve a fidelity of 95%. This work helps with the long-term preservation of massive Chinese character archives.

Skeleton extraction of hard‐pen regular script based on stroke characterization and ambiguous zone detection

AbstractThe Intelligent Evaluation System for Calligraphy Characters (IESCC) is used for teaching calligraphy, and users can learn calligraphy through the modifications given by the system. Chinese character skeleton extraction is an important step in the intelligent evaluation algorithm of calligraphic characters. The skeletons of Chinese characters extracted by traditional refinement algorithms are prone to redundant branches and deformed skeletons, which can lead to skeleton extraction results that do not conform to the topology of the original character. In this study, the focus lies on hard‐pen regular script, and skeleton repair and extraction are performed for these characters. According to the writing characteristics of regular script, the redundant burs are removed and the deformation zone of the thinned skeleton is detected, and then the idea of first splitting is used, then restructuring, to propose a skeleton extraction algorithm based on stroke characterization and ambiguous zone detection for hard‐pen regular script, referred to as SCAD. First, a thinning algorithm is used to extract the skeleton of Chinese characters and remove redundant pixels. By analyzing the stroke characteristics of regular script, the burrs are classified and different conditions are set to detect and remove the burrs. Then the ambiguous zones are detected according to the different kinds of junction points. Then, curvature, stroke width and direction deviation are used to analyze the continuity of stroke segments, and the decision function is used to classify the stroke segments. Finally, the stroke segments with optimal pairings were compensated by interpolation according to the direction trend. This concludes the skeleton extraction. Skeleton extraction is performed on 1000 sample characters, and the SCAD algorithm can extract the skeleton of Chinese characters with an accuracy of up to 98.37%. It is proved that the SCAD method proposed here is a practical and effective method to extract the skeleton of hard‐pen regular script.

Legibility of Sans-Serif Typeface on Different Paper Grades Made from Invasive Alien Plant Species

Invasive alien plant species (IAPS) may cause threats to native biodiversity in ecosystems. Researchers have been investigating all the possible ways that they can be used effectively for other purposes. Since IAPS are capable of forming cellulose fibre nets, in this research, papers were made from three different types of IAPS (Japanese knotweed, giant goldenrod, and black locust). This research examined these IAPS papers and their effectiveness when used as printing substrates. In comparison to commercial office paper, the differences in basic, surface, optical, and microscopic properties were measured. As a widely used technology, inkjet printing was applied. We tested a commonly used sans-serif typeface (which has been established as being more legible than other typefaces in previous research) in three different type sizes (i.e., 8, 10, and 12 pt). According to the results, paper made from IAPS could offer some usable properties and acceptable legibility, especially when printing typefaces with specific attributes, such as moderate counter size, higher x-height, and minimal differences in the letter stroke width, are used. An appropriate typographic tonal density should be achieved in combination with an adequate letter size, e.g., 10 pt type size when a sans-serif typeface is used.

Abstract 124: Pre-Stroke Aspirin Use Does Not Impact Small Subcortical Stroke Size: A Secondary Analysis of the SPS3 Trial

Introduction: The use of pre-stroke aspirin is associated with smaller stroke size in first-time stroke related to large vessel occlusion. However, it is unknown whether pre-stroke aspirin use impacts stroke size for an acute small subcortical infarct (SSI). We hypothesized that pre-stroke aspirin use would be associated with a smaller sized SSI. Methods: We performed a secondary analysis of the Secondary Prevention of Small Subcortical Strokes (SPS3) dataset which enrolled between 2003 and 2007. We included participants ≥ 55 years old with brain-MRI confirmed SSI being ≤ 20 mm in maximal diameter on diffusion weighted imaging. Stroke size was determined retrospectively by the study team on the index event brain MRI. Pre-stroke medications were recorded at the time of enrollment. Chi-squared and student’s T test were used for categorical and continuous variables, respectively. Binary logistic regression models were fitted for likelihood ratios. Results: Of the 3020 participants enrolled in SPS3, a total of 2183 met inclusion criteria: 663 with pre-stroke aspirin monotherapy and 1520 without pre-stroke antithrombotic use, Table 1. Those with aspirin use were older with a higher incidence of diagnoses of hypertension, hyperlipidemia, and diabetes mellitus, though otherwise sex, and tobacco use did not differ. The mean maximal stroke width, trended towards, but was not statistically impacted by pre-stroke aspirin use [pre-stroke aspirin: 11.8 (4.1) mm vs 11.4 (4.1) mm, p =0.065]. After adjustment for significant variables, pre-stroke aspirin use did not impact the odds of a larger infarct size (OR 0.779, 95%CI 0.615-1.04, p =0.092). Conclusion: Despite those with pre-stroke aspirin use having more vascular risk factors, its use did not impact stroke size amongst those with an acute SSI enrolled in the SPS3 trial.

The Relationship Between Recurrent Cerebrovascular Stroke and Erythrocyte Distribution Width in Atrial Fibrillation Patients with Cerebrovascular Stroke

Amaç: Atriyal fibrilasyon tromboembolik olaylara sebep olan bir sık görülen bir aritmi türüdür.
 Kardiyoembolik inme önemli bir morbidite ve mortalite nedenidir. Kırmızı kan hücresi dağılım genişliği
 (KDG, ‘Red blood cell distribution width’:RDW), kronik inflamasyon ve yüksek oksidatif stres durumunda
 artabilen bir belirteçtir. Çalışmamızda akut iskemik inme geçiren atriyal fibrilasyon hastalarında KDG
 düzeyi ile tekrarlayan erken serebrovasküler inme sıklığı arasındaki ilişkiyi incelemek amaçlandı.
 Gereç ve Yöntemler: İskemik inme tanısı alarak nöroloji servisine yatış yapılan, kardiyoloji
 konsültasyonu esnasında elektrokardiyografisinde atriyal fibrilasyon saptanan hastalar çalışmaya
 dahil edildi. Hastaların kronik hastalık durumu, kullanılan ilaçlar, yatış sırasında rutin yapılmış olan kan
 tetkikleri, ekokardiyografi bulguları kayıt altına alındı. Hastalar 3 ay süresince takip edildi.
 Bulgular: Çalışmaya dahil edilen 164 hastanın yaş ortalaması 73,6 ± 9,3 idi. Hastaların 97’si (%59,1)
 kadın, 67’si (%40,9) erkek cinsiyette idi. Doksan günlük takip sonucunda 31(%18,9) hastanın tekrarlayan
 iskemik inme geçirdiği saptandı. Tekrarlayan inme geçirenler ile geçirmeyenler arasında yaş, cinsiyet,
 kronik hastalık, dislipidemi, sigara kullanımı, ejeksiyon fraksiyonu, CHA2DS2-VASc skoru, NIHHS
 Skoru düzeyleri, serum CRP düzeyleri, kan trombosit sayısı arasında istatistiksel olarak anlamlı fark
 saptanmazken; KDG düzeyleri (14,6 ± 1,4 vs 13,3 ± 0,8) tekrarlayan inme geçiren hastalarda daha
 yüksek saptandı (p

Feature fusion-based text information mining method for natural scenes

Abstract As a crucial medium of information dissemination, text holds a pivotal role in a multitude of applications. However, text detection in complex and unstructured environments presents significant challenges, such as the presence of cluttered backgrounds, variations in appearance, and uneven lighting conditions. To address this issue, this study proposes a text detection framework that leverages multistage edge detection and contextual information. This framework deviates from traditional approaches by incorporating four primary processing steps, including text visual saliency region detection to accentuate the text regions and diminish background interference, multistage edge detection to enhance the conventional stroke width transform results, a texture-based and connected components-based integration to accurately distinguish text from the background, and a context fusion step to recover missing text regions and improve the recall of text detection. The proposed method was evaluated on two widely used benchmark datasets, i.e., the international conference on document analysis and recognition (ICDAR) 2005 dataset and the ICDAR 2011 dataset, and the results indicate the advancedness of the method.

Robotic Writing of Arbitrary Unicode Characters Using Paintbrushes

Human handwriting is an everyday task performed regularly by most people. In the domain of robotic painting, multiple calligraphy machines exist which were built to replicate some aspects of human artistic writing; however, most projects are limited to a specific style of handwriting, often Chinese calligraphy. We propose a two-stage pipeline that allows industrial robots to write text in arbitrary typefaces and scripts using paintbrushes. In the first stage, we extract a set of strokes from character glyphs which are similar to how humans choose strokes during writing. In the second stage, we generate corresponding brush trajectories by applying a brush model to the extracted strokes. Our brush model computes the required brush pressure to achieve the given stroke width while also accounting for brush lag. We also present a method to automatically measure the parameters needed to predict brush lag by painting and recording calibration patterns. Our method generates trajectories for text in any given typeface, which, when executed by a robotic arm, results in legible written text. We can render most writing systems, excluding emoji and ligatures, in which arbitrary texts can be specified to write.

Simulation Model for Robotic Pick-Point Evaluation for 2-F Robotic Gripper

Robotic bin-picking performance has been gaining attention in recent years with the development of increasingly advanced camera and machine vision systems, collaborative and industrial robots, and sophisticated robotic grippers. In the random bin-picking process, the wide variety of objects in terms of shape, weight, and surface require complex solutions for the objects to be reliably picked. The challenging part of robotic bin-picking is to determine object pick-points correctly. This paper presents a simulation model based on ADAMS/MATLAB cosimulation for robotic pick-point evaluation for a 2-F robotic gripper. It consists of a mechanical model constructed in ADAMS/View, MATLAB/Simulink force controller, several support functions, and the graphical user interface developed in MATLAB/App Designer. Its functionality can serve three different applications, such as: (1) determining the optimal pick-points of the object due to object complexity, (2) selecting the most appropriate robotic gripper, and (3) improving the existing configuration of the robotic gripper (finger width, depth, shape, stroke width, etc.). Additionally, based on this analysis, new variants of robotic grippers can be proposed. The simulation model has been verified on a selected object on a sample 2-F parallel robotic gripper, showing promising results, where up to 75% of pick-points were correctly determined in the initial testing phase.

The effect of serifs and stroke contrast on low vision reading

PurposePatients with low vision are generally recommended to use the same fonts as individuals with normal vision. However, we are yet to fully understand whether stroke width and serifs (small ornamentations at stroke endings) can increase readability. This study's purpose was to characterize the interaction between two factors (end-of-stroke and stroke width) in a well-defined and homogenous group of patients with low vision. MethodsFont legibility was assessed by measuring word identification performance of 19 patients with low vision (autosomal dominant optic atrophy [ADOA] with a best-corrected average visual acuity 20/110) and a two-interval, forced-choice task was implemented. Word stimuli were presented with four different fonts designed to isolate the stylistic features of serif and stroke width. ResultsFont-size threshold and sensitivity data revealed that using a single measure (i.e., font-size threshold) is insufficient for detecting significant effects but triangulation is possible when combined with signal detection theory. Specifically, low stroke contrast (smaller variation in stroke width) yielded significantly lower thresholds and higher sensitivity when a font contained serifs (331 points; d’ = 1.47) relative to no serifs (345 points; d’ = 1.15), E(μsans, low − μserif, low) = −14 points, 95 % Cr. I. = [−24, −5], P(δ > 0) = 0.99 and E(μserif, low − μsans, low) = 0.32, 95 % Cr. I. = [0.16, 0.49], P(δ > 0) = 0.99. ConclusionIn people with low visual acuity caused by ADOA, the combination of serifs and a uniform stroke width resulted in better text legibility than other combinations of uniform/variable stroke widths and presence/absence of serifs.

River extraction from Gaofen-1 Satellite images combined with relative total variation and stroke width transform

Extraction of rivers from remote sensing images is crucial for understanding the utilization of water resources. Because rivers have similar and consistent widths in a consistent range of remote sensing images, the stroke width transform (SWT) algorithm has a good effect on river extraction. However, the algorithm is susceptible to the influence of features around the river. A river extraction method combining relative total variation (RTV) and SWT was proposed to obtain river area information with high accuracy. First, the near-infrared channel of the image was selected as the input and the RTV model was used for texture smoothing and edge enhancement. Thereafter, the image edge map calculateds by the Canny operator was used as the input of the SWT to obtain the stroke width map. Subsequently, the connectivity components were labeled, and a specific filter was constructed according to the geometric characteristics of the river to filter out nonriver noise. The retained connected components were filled with holes, and a highly accurate river basin map was generated. In this study, six images of rivers flowing through four different landforms, including woodland, urban, mountainous, and cultivated land, were captured by the Gaofen-1 satellite for validation and comparison analysis. The experimental results demonstrated that the method’s completeness, accuracy, and extraction quality in this study could be maintained above 95%. Compared with the mainstream methods of the Otsu method, normalized difference water index, U-Net, and direct use of the SWT algorithm, the method in this study is significantly better in terms of extraction effect and algorithm stability. This can effectively improve the accuracy of river segmentation.

문서 감정상 볼펜 필획의 잉크 밀집도 및 너비 특성

The purpose of this study was to confirm the possibility of using the ink stroke density and width as a non-destructive analysis factor for estimating the ink and ball size of a ballpoint pen in the field of identification of writing instruments for document evaluation. The density and width of strokes were measured by applying a constant pressure (100-300 g) to a total of 39 ballpoint pens of 4 types of ink (oil, low-viscosity oil, gel and water) and ball size (0.5-1.0 mm). As a morphological characteristic of stroke density, it was confirmed that there were four types: 1) Selective diffusion of high-viscosity ink, 2) Agglomeration, 3) Dot, 4) Selective diffusion of low-viscosity ink. The deposition value of ink stroke density showed a tendency to be measured twice as low for oil (33.74~47.26%) with high viscosity compared to gel (78.19~84.66%) and water (44.96~85.26%) with low viscosity of ink. The stroke width showed a tendency to increase as the pressure applied to the ballpoint pen and the size of the ball increased. The stroke width according to the ink was similar in the case of oily and low-viscosity oils, but in the case of neutral, it was about 100 μm higher than that of oil and low-viscosity oils. As a result, the density and width of strokes showed a difference in the characteristics that appeared according to the viscosity of the ink. In the order of non-destructive analysis for the identification of writing instruments, it is necessary to ① analyze the deposit shape and deposit value of ink stroke density, and ② to estimate the ink and ball size through ink width measurement. It was confirmed that it can be used.

Forgery-free signature verification with stroke-aware cycle-consistent generative adversarial network

In recent years, the performance of handwritten signature verification (HSV) has been considerably improved by deep learning methods. However, deep HSV still faces significant challenges due to the lack of training data, especially for skilled forgeries. In this context, signature synthesis is a promising alternative to address the problem of insufficient data. Compared with offline modality, online signatures are more likely to produce natural duplicates by virtue of their dynamic information. Therefore, we propose a novel convolutional neural network model for offline HSV, called SigCNN, and utilize CycleGAN in style transfer fields to generate realistic offline signatures from online specimens and their duplicates. To compensate for the deficiency of vanilla CycleGAN in generating diverse stroke widths, we propose a new method, Stoke-cCycleGAN, to generate signatures at desired stroke width levels. By online signature duplication and online-to-offline conversion, our SigCNN model can be trained without requiring skilled forgeries. Experimental results showed that our SigCNN trained on generated signatures achieved competitive results on public datasets compared to existing methods. Code of Stroke-cCycleGAN is available at https://github.com/KAKAFEI123/Stroke-cCycleGAN.

MODELING OF PRESSURE IN WRITING AND THE POSSIBILITY OF EVALUATING THE PRESSURE CHARACTERISTICS OF HANDWRITING

The article considers a physical-and-mechanical model of the writing process using a writing device with a ball element. The purpose of developing this model is to develop the theory of pressure in judicial handwriting, as well as to evaluate the pressure characteristics of handwriting, which are manifested primarily in the width and depth of handwritten strokes. At the same time, it is concluded that it is necessary to study handwriting objects from the perspective of their three-dimensionality, since it is the pressing characteristics of handwriting in this three-dimensionality that manifest themselves and allow for the further development of three-dimensional models of the writing process and the use of appropriate tools (technologies) to increase the reliability and objectivity of expert conclusions in the production of forensic handwriting examinations. The development of a physical-and-mechanical model reflecting the writing process is a very urgent task in judicial handwriting, since writing devices with a ball element (ballpoint pens, gel pens, roller pens) are currently used in writing much more often than others. The author offers a new definition of the concept of "pressure when writing", which takes into account both the physics of the writing process and its result according to the theory of trace formation adopted in traceology. He also demonstrated examples of calculating the pressure parameters manifested in the relief of handwritten strokes, outlined new approaches to the analysis and evaluation of the pressure characteristics of handwriting.

Text line segmentation from struck-out handwritten document images

In the case of freestyle everyday handwritten documents, writing, erasing, striking out, and overwriting are common behaviors of the writers. This not cleanly-written text poses significant challenges for text line segmentation. Accurate text line segmentation in handwritten documents is essential to the success of several real-world applications, such as answer script evaluation, fraud document identification, writer identification, document age estimation and writer gender classification, to name a few. This paper proposes the first, to the authors’ best knowledge, text line segmentation approach that is applicable in the presence of both cleanly-written and struck-out text. The approach consists of three steps. In the first step, components - at the word level - are detected in the input handwritten document images (containing both cleanly-written and struck-out text) based on stroke width information estimation, filtering of noise, and morphological operations. In the second step, the struck-out components are identified using the DenseNet deep learning model and treated differently to clean text in further analysis. In the third step, geometrical spatial features, the direction between candidate components and the overall text line, and the common overlapping region between adjacent components are evaluated to progressively form text lines. To evaluate the proposed steps and compare the proposed method to the state-of-the-art, experiments have been conducted on a new problem-focused dataset containing instances of struck-out text in handwritten documents, as well as on two standard datasets (ICDAR2013 text line segmentation contest dataset and ICDAR2019 HDRC dataset) to show the proposed steps are effective and useful, with superior performance compared to existing methods.

Effect of NOTCH3 EGFr Group, Sex, and Cardiovascular Risk Factors on CADASIL Clinical and Neuroimaging Outcomes.

Background:A retrospective study has shown that EGFr (epidermal growth factor–like repeat) group in the NOTCH3 gene is an important cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) disease modifier of age at first stroke and white matter hyperintensity (WMH) volume. No study has yet assessed the effect of other known CADASIL modifiers, that is, cardiovascular risk factors and sex, in the context of NOTCH3 EGFr group. In this study, we determined the relative disease-modifying effects of NOTCH3 EGFr group, sex and cardiovascular risk factor on disease severity in the first genotype-driven, large prospective CADASIL cohort study, using a comprehensive battery of CADASIL clinical outcomes and neuroimaging markers.Methods:Patients with CADASIL participated in a single-center, prospective cohort study (DiViNAS [Disease Variability in NOTCH3 Associated Small Vessel Disease]) between 2017 and 2020. The study protocol included a clinical assessment, neuropsychological test battery and brain magnetic resonance imaging on a single research day. Multivariable linear, logistic and Cox regression models were used to cross-sectionally assess the effect of CADASIL modifiers on clinical severity (stroke, disability, processing speed) and neuroimaging markers (WMH volume, peak width of skeletonized mean diffusivity, lacune volume, brain volume, cerebral microbleed count).Results:Two hundred patients with CADASIL participated, of which 103 harbored a NOTCH3 EGFr 1–6 variant and 97 an EGFr 7–34 variant. NOTCH3 EGFr 1–6 group was the most important modifier of age at first stroke (hazard ratio, 2.45 [95% CI, 1.39–4.31]; P=0.002), lacune volume (odds ratio, 4.31 [95% CI, 2.31–8.04]; P=4.0×10-6), WMH volume (B=0.81 [95% CI, 0.60–1.02]; P=1.1×10-12), and peak width of skeletonized mean diffusivity (B=0.65 [95% CI, 0.44–0.87]; P=1.6×10-8). EGFr 1–6 patients had a significantly higher WMH volume in the anterior temporal lobes and superior frontal gyri and a higher burden of enlarged perivascular spaces. After NOTCH3 EGFr group, male sex and hypertension were the next most important modifiers of clinical outcomes and neuroimaging markers.Conclusions:NOTCH3 EGFr group is the most important CADASIL disease modifier not only for age at first stroke and WMH volume but also strikingly so for a whole battery of clinically relevant disease measures such as lacune volume and peak width of skeletonized mean diffusivity. NOTCH3 EGFr group is followed in importance by sex, hypertension, diabetes, and smoking.

Automatic image positioning of a rail train number using speed-up robust features and stroke width transform

The rail train number is the only sign information of the vehicle. It is very important to use the image processing method to locate the train number to improve the efficiency of rail train management. Therefore, an automatic image positioning framework for rail train numbers is developed. First, aiming at the problem of low contrast between the rail train number and vehicle body caused by insufficient illumination of the rail train in a tunnel and indoor, the improved single-scale Retinex algorithm based on brightness control is used to enhance the image, which provides the basis for subsequent train number image positioning. Then, a train number location algorithm based on speed-up robust features and stroke width transform is proposed to locate the train number accurately. The experimental results show that the accuracy of the developed method is 96.91% in the complex environment of uneven illumination and large distortion of rail train numbers.

Sprint Performance in Arms-Only Front Crawl Swimming Is Strongly Associated With the Power-To-Drag Ratio.

To date, optimal propulsion in swimming has been studied predominantly using physical or computational models of the arm and seldom during real-life swimming. In the present study we examined the contributions of selected power, technique and anthropometric measures on sprint performance during arms-only front crawl swimming. To this end, 25 male adult competitive swimmers, equipped with markers on their arms and hands, performed four 25-m sprint trials, which were recorded on video. For the fastest trial of each swimmer, we determined the average swim speed as well as two technique variables: the average stroke width and average horizontal acceleration. Each participant also swam 10–12 trials over a custom-made system for measuring active drag, the MAD system. Since the propelling efficiency is 100% while swimming over the MAD system, the power output of the swimmer is fully used to overcome the drag acting on the body. The resulting speed thus represents the ratio between power output and drag. We included this power-to-drag ratio, the power output and the drag coefficient of the fastest trial on the MAD system in the analysis. Finally, the body height and hand surface area of each swimmer were determined as anthropometric variables. A model selection procedure was conducted to predict the swim speed from the two technique variables, three power variables and the two anthropometric variables. The ratio between power output and the drag was the only significant predictor of the maximal swimming speed (v = 0.86·power/drag). The variations in this ratio explained 65% of the variance in swimming performance. This indicates that sprint performance in arms-only front crawl swimming is strongly associated with the power-to-drag ratio and not with the isolated power variables and the anthropometric and technique variables selected in the present study.

A Text Location Method for Sign Based on Pixel-level Gradient Ray Images and Edge Contour Mapping

Compared with deep learning methods, traditional image processing methods have lower computational costs and have advantages for typical problems such as sign text, this paper proposes a sign text location method based on pixel-level gradient ray map and edge contour mapping. First, based on image pre-processing, the pixel-level gradient ray map is obtained by using the Stroke Width Transform. Then, using the Maximally Stable Extremal Region method to locate the irregular text candidate area in the ray map. At the same time, add a mask layer to filter the irregular candidate area, and then regularize the candidate area and perform Non-Maximum Suppression processing. Finally, the background non-text candidate area is processed by extracting the mapping of the closed edge contour of the image, and the remaining text area positioning candidate frame coordinates are mapped back to the original picture. The experimental results show that the method is simple and clear, the effect is remarkable, and it has good robustness to the sign of different sizes.

Patients' Self-Report and Handwriting Performance Features as Indicators for Suspected Mild Cognitive Impairment in Parkinson's Disease.

Early identification of mild cognitive impairment (MCI) in Parkinson’s disease (PD) patients can lessen emotional and physical complications. In this study, a cognitive functional (CF) feature using cognitive and daily living items of the Unified Parkinson’s Disease Rating Scale served to define PD patients as suspected or not for MCI. The study aimed to compare objective handwriting performance measures with the perceived general functional abilities (PGF) of both groups, analyze correlations between handwriting performance measures and PGF for each group, and find out whether participants’ general functional abilities, depression levels, and digitized handwriting measures predicted this CF feature. Seventy-eight participants diagnosed with PD by a neurologist (25 suspected for MCI based on the CF feature) completed the PGF as part of the Daily Living Questionnaire and wrote on a digitizer-affixed paper in the Computerized Penmanship Handwriting Evaluation Test. Results indicated significant group differences in PGF scores and handwriting stroke width, and significant medium correlations between PGF score, pen-stroke width, and the CF feature. Regression analyses indicated that PGF scores and mean stroke width accounted for 28% of the CF feature variance above age. Nuances of perceived daily functional abilities validated by objective measures may contribute to the early identification of suspected PD-MCI.