Handwritten text recognition, particularly challenging due to the variability in handwriting styles, is critical for digitizing handwritten content like historical manuscripts, forms, and personal notes. So, this study aims to compare several existing handwritten text and character recognition methods from document images using machine learning and deep learning techniques, including convolutional neural networks, recurrent neural networks, and ensemble learning techniques. The primary concepts of ensemble techniques are integrating multiple classifiers to enhance predictive performance and to provide better generalization over diverse datasets. Further, this study discusses various applications of handwritten text recognition, including form processing, cheque reading, postal systems, medical records digitization, and signature verification. Additionally, it addresses challenges in handwritten text recognition, such as noise in handwritten text, non-uniform datasets, and contextual understanding of handwritten material, thereby leading toward proposing solutions using advanced algorithms. Further, it includes a detailed literature review of existing handwritten text methods highlighting the efficiency of ensemble methods and convolutional neural network-based techniques in achieving up to 99.88% accuracy on benchmark datasets. The study concludes that although ensemble approaches significantly improve text recognition from handwritten images, marking a substantial advancement in document image-processing technologies, there is still a need to work on a highly efficient, generic, language-independent, and robust algorithm for handwritten text recognition.