Dysgraphia is a learning disability of written expression, which affects the ability to write, mainly handwriting and coherence. Several studies have proposed approaches for assisting dysgraphic people based on AI algorithms. However, existing aids for dysgraphia take only one aspect of the problem faced by the patients into consideration. Indeed, while some provide writing assistance, others address spelling or grammatical problems. In this paper, a novel system for helping people suffering from dysgraphia is proposed. Our system tackles several problems, such as spelling mistakes, grammatical mistakes and poor handwriting quality. Further, a text-to-speech functionality is added to improve the results. The proposed system combines a plethora of solutions into a valuable approach for efficient handwriting correction: handwritten text recognition using a CNN-RNN-CTC model, a spelling correction model based on the SymSpell and Phoneme models, and a grammar correction using the GECToR model. Three machine learning models are proposed. The experimental results are compared based on the values of Character error rate, Word error rate, and the workflow of three handwritten text recognition models, and has led to an improvement of the quality of the results.