This study investigates the classification of two prominent movement disorders: Parkinson’s Disease (PD) and Essential Tremor (ET) using a comprehensive machine learning framework. A novel dataset was meticulously created which contains handwriting samples collected at Habib Bourguiba Hospital in Sfax, Tunisia, specifically designed for differentiating between PD and ET. Preprocessing techniques such as image resizing, normalization, and data augmentation were employed to enhance robustness. Feature extraction was performed using the ResNet50 model, effectively capturing essential image characteristics through global average pooling. Recursive Feature Elimination (RFE) was then applied to identify the most significant features, followed by the training and validation of two classification models Random Forest and SVM using these selected features. The performance of these models is rigorously assessed through various metrics, revealing that the Random Forest model attained an accuracy of $92.83 \% \pm 2$, while the SVM model achieved an average accuracy of $94.66 \% \pm 1$. Visualizations such as confusion matrices and ROC curves provide deeper insights into model performance. Overall, the findings demonstrate the potential of machine learning techniques to enhance diagnostic accuracy in distinguishing between PD and ET, ultimately contributing to improved clinical decision-making.

Parkinson’s disease (PD) is a common and progressive neurodegenerative disorder with motor symptoms and a variety of non-motor symptoms. Experts regularly include handwriting as one of the Parkinsonian motor symptoms of PD and as a valuable tool that can aid in diagnosing and tracking the disease’s progression. PD patients have two periods. ‘On’ time is when levodopa is working well and your symptoms are controlled. ‘Off’ time is when levodopa is no longer working well and symptoms such as tremor, rigidity and slow movement re-emerge. To our knowledge, all existing publicly available datasets allow PD to be identified using only one period. No publicly available online handwriting datasets are dedicated to the analysis of PD using these two periods. Therefore, in this paper, we present our new online Arabic handwriting dataset for analysing PD, which we will make publicly available so that it could potentially be used for diagnosis, screening and monitoring the progression of PD. Our dataset was collected from 30 healthy controls and 30 PD patients in both “off” and “on” at the Neurology Department, Habib Bourguiba Hospital, Sfax, Tunisia. All participants performed five different handwriting tasks. The tasks included drawing repetitive ellipses, a spiral, repetitive digits and Arabic word writing. We hope that our new dataset will help researchers in the early detection of Parkinson’s disease, inpatient rehabilitation and quantification of therapeutic effects.

The segmentation of online handwritten Arabic text into graphemes/characters is a challenging task for the recognition system due to the nature of this script. That is why, it is better to employ dependency in the context of segments written before and after it, to improve recognition accuracy. In this paper, we introduce Multi-Head Self-Attention (MHSA) and Bidirectional Gated Recurrent Units (BGRU) models for online handwritten Arabic text segmentation, both of which simulate our previous grapheme segmentation model (GSM). The proposed framework consists of word embedding and the combination of complementary Multi-Head Self-Attention and BGRU, which help detect the control points (CPs) for handwritten text segmentation. The CPs delimit each grapheme composed of three main geometric points: starting point (SP), ligature valley point (LVP), angular point (AP), and ending point (EP). To show the effectiveness of our MHSA-BGRU model for online handwritten segmentation and its comparison with GSM, both mean absolute error (MAE), and word error rate (WER) evaluation metrics are used. Experimental results on benchmark ADAB and online-KHATT datasets show the efficiency of our model, which achieves 3.17% and 5.28% for MAE, 12.25% and 25.13% for WER respectively.

Compared to traditional biometric systems, in-air signatures are considered more robust and secure than classical pen paper. A few datasets capturing in-air signatures have been introduced, utilizing various devices such as the Leap Motion and the Microsoft Kinect sensor camera. However, these devices are not exempt from shortcomings and exhibit certain limitations. The expenses associated with their implementation and the requirement for technical proficiency in operating them present notable challenges for in-air signature analysis. Additionally, users may encounter difficulties in adapting their finger movements to fit within the device's limited field of view, particularly if they lack familiarity with these devices. To address these concerns, this paper proposes the creation of three in-air signature datasets using solely the camera of a laptop or a smartphone, eliminating the need for any additional specialized equipment. Our datasets were collected in three ways. The first is the In-Air Signature dataset (IAS dataset) and the second is the In-Air Signature dataset using a transparent Glass Plate (IASGP dataset) while the third is the In-Air Signature dataset using Smart Phone (IASSP dataset). Forty volunteers participated in the construction of these datasets. Their ages ranged from 21 to 40 years. Each volunteer signs in the air five signatures and imitates five signatures of five other volunteers. Our in-air signatures datasets are publicly available and can be used for various research tasks like in-air signature verification and identification.

The deep learning-based approaches have proven highly successful in handwriting recognition which represents a challenging task that satisfies its increasingly broad application in mobile devices. Recently, several research initiatives in the area of pattern recognition studies have been introduced. The challenge is more earnest for Arabic scripts due to the inherent cursiveness of their characters, the existence of several groups of similar shape characters, large sizes of respective alphabets, etc. In this paper, we propose an online Arabic character recognition system based on hybrid Beta-Elliptic model (BEM) and convolutional neural network (CNN) feature extractor models and combining deep bidirectional long short-term memory (DBLSTM) and support vector machine (SVM) classifiers. First, we use the extracted online and offline features to make the classification and compare the performance of single classifiers. Second, we proceed by combining the two types of feature-based systems using different combination methods to enhance the global system discriminating power. We have evaluated our system using LMCA and Online-KHATT databases. The obtained recognition rate is in a maximum of 95.48% and 91.55% for the individual systems using the two databases respectively. The combination of the on-line and off-line systems allows improving the accuracy rate to 99.11% and 93.98% using the same databases which exceed the best result for other state-of-the-art systems.

The online writer identification is a required component in many applications of Computer vision and Pattern Recognition. The offline writer identification is more developed in literature due to the use of traditional system based on Image Processing. There is a lack of works done in the case of online writer identification. In this paper, we propose a novel method to text independent writer identification from online handwriting. Our proposed method is based on the use of Beta-elliptic model that computes efficiently on real time writing movements in online handwriting by involving simultaneously its both profile entities: the Beta impulses and the elliptic arcs. The information provided by the feature extraction is used in a Deep Neural Network as classifier. The obtained results show that the proposed online writer identification method is worth to receive further exploration in capturing the writer's individual. The use of the Deep Neural Network provides more robustness to identification of writers.

This paper proposes an automatic text-independent online Arabic writer identification system. The main contribution of our system is to explore the utility of Beta-elliptic model in features extraction for online writer identification, due to the rich output of Beta-elliptic model in terms of graphical, kinematical and biometrical data. The efficiency of the considered features has been evaluated using feed forward neural network classifier. Experimental results on ADAB Database show the performance of the proposed system in online Arabic writer identification task.