In artificial intelligence and machine learning, handwriting recognition bridges the gap between handwritten input and digital interpretation. Methods such as neural networks, layered convolutional models, and specialized architectures drive advancements in this field. This paper examines the role of diverse scripts, language processing, and custom-designed models. Key improvements focus on deep learning frameworks, hybrid techniques, and language-based optimizations. Current research trends include the application of dropout in RNNs and convolutional layers and incorporating AI structures to enhance performance. This survey underscores key studies in handwriting recognition, targeting greater precision and efficiency in upcoming systems.