Handwriting recognition remains a complex and evolving field in machine learning, presenting challenges that include variability in handwriting styles, document degradation, and the difficulty of collecting labeled datasets. Despite these hurdles, the market for optical character recognition (OCR) is projected to grow significantly, driven by its applicability across diverse industries such as healthcare, insurance, banking, and historical document digitization. Traditional machine learning methods like Hidden Markov Models have been supplemented by modern deep learning techniques, including multi-dimensional LSTMs, attention-based models, and transformers, to improve accuracy. Furthermore, new methods like Generative Adversarial Networks (GANs) are being explored to generate synthetic training data, which helps overcome data scarcity. While handwriting text recognition (HTR) has not yet achieved the widespread adoption seen with OCR, advancements in deep learning and transformer models suggest that its widespread implementation may be on the horizon.