The Khmer script presents a unique challenge due to its distinct characters, which make recognizing handwriting especially challenging. This study tackles this challenge by creating a specialized computer program that recognizes handwritten Khmer text. Our goal is to enhance Khmer language education by offering a valuable tool that supports educators and students in improving their Khmer language proficiency. This user-friendly tool improves handwriting skills and the program's adaptability and accuracy across handwriting styles, making learning more stimulating and effective. To accomplish this objective, we are beginning an extensive data collection initiative, creating a diverse dataset of handwritten Khmer samples. This dataset forms the foundation of our program's training, facilitating precise recognition. Despite the size of the dataset and the computational resource requirements, we are optimistic about this tool's potential impact on Khmer language education. Beyond improving handwriting, our program aims to improve Khmer educational material accessibility and efficiency. Utilizing a sequence-to-sequence-based model, particularly the LSTM-based encoder-decoder architecture, demonstrates our commitment to achieving the highest accuracy. Our model's performance is evaluated using the character error rate (CER), a metric that represents its precision in recognizing individual characters. With a dataset comprising 50,720 Khmer words, our model currently achieves an error rate of 3.36%, highlighting its effectiveness. By preserving and developing the Khmer language with advanced neural networks, our Khmer handwriting recognition tool preserves culture and promotes education in Cambodia. This tool has practical implications for enhancing teaching and learning experiences in educational settings by making educational materials more accessible and providing instant feedback on handwriting and text recognition.