###### Abstract  
Parkinson's disease (PD) Dysgraphia is a disorder that affects most PD patients and is characterized by handwriting anomalies caused mostly by motor dysfunctions. Several effective ways to quantify PD dysgraphia analysis have been used, including online handwriting processing. In this research, an integrated approach, using a combination of a convolutional neural network (CNN) and long short-term memory (LSTM) layers along with a Random Forest (RF) classifier, is proposed for dysgraphia classification. The proposed approach uses uniform and normal distributions to randomly initialize the weights and biases of the CNN and LSTM layers. The CNN-LSTM model predictions are paired with the RF classifier to enhance the model's accuracy and endurance. The suggested method shows promise in identifying handwriting symbols for those with dysgraphia, with the CNN-LSTM model's accuracy being improved by the RF classifier. The suggested strategy may assist people with dysgraphia in writing duties and enhance their general writing skills. The experimental results indicate that the suggested approach achieves higher accuracy. Keywords:handwritten characters recognition; Random Forest; CNN; LSTM +
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Handwritten character recognition for dysgraphia patients, especially the identification of broken text, is a relatively challenging task. The use of machine learning and deep learning approaches has resulted in a significant rise in the text recognition rate [12; 13; 14]. Recent development in digital image processing and machine/deep learning models have shown good results in various applied domains that can be used in OCR [15; 16; 17; 18]. Recurrent Neural Networks (RNNs) may generate contextual knowledge by translating all preceding data to each output and constructing a memory using their data sequencing approach. The Convolution-Neural-Network (CNN) is a deep learning architecture that is physiologically inspired by the human brain's sensory perception process. Convolution layers are being used for knowledge selection. CNN layers are classified into three types: convolutional, fully connected, and pooling. The most extensively used deep learning algorithms are CNN and Long Short-Term Memory (LSTM). Deep learning architectures enable more sophisticated models to be learned in detail to retrieve hierarchical characteristics from data than typical machine learning methods, which conduct a linear transformation of nonlinear feature extraction on a single layer [19]. The proposed approach in this study has considerable implications for people who have dysgraphia, a neurological condition that affects writing ability. The hand-written character recognition system can help people with dysgraphia write more quickly and accurately. The CNN and LSTM layers' weights and biases are arbitrarily initialized using uniform and normal distributions, which aids in avoiding local minima and improves the model's overall performance. Additionally, the CNN-LSTM model's final layer's feature extraction procedure enables the extraction of significant and instructive features that can be applied to classification using a Random Forest model. The importance of this research for people with dysgraphia lies in its ability to enhance their general quality of life. Dysgraphia can have an effect on several aspects of quality of life, such as social interactions, employment, and schooling. The suggested method can help people with dysgraphia become more proficient writers, which can enhance their success in school and at work. Additionally, this study may have an impact on enhancing the technology's usability for people with impairments. Overall, the suggested method may have significant effects on raising the standard of living for people who have dysgraphia and other writing disabilities. The rest of the paper is organized as follows: Section 2 discusses the previously reported deep learning algorithms for handwritten character recognition.