Dysgraphia is a written expression learning deficit that primarily impacts handwriting and coherence. Numerous studies have put forth AI-based strategies for helping dysgraphic individuals. Currently available dysgraphia aids, however, only address one facet of the patients’ issues. In fact, some offer writing help, while others deal with grammatical or spelling issues. This research proposes a revolutionary system to assist individuals with dysgraphia. Our method addresses a number of issues, including poor handwriting quality, grammatical errors, and spelling errors. To enhance the outcomes, a text-to-speech feature is also included. Using a CNN-RNN-CTC model for handwritten text recognition, a SymSpell and Phoneme model for spelling correction, and a GECToR model for grammatical correction, the suggested system integrates a variety of solutions into a useful method for effective handwriting correction. This paper uses character error rate, word error rate, and the workflow of three handwritten text recognition models to compare the experimental findings, which has improved the quality of the output.