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As deep learning and image processing have advanced, handwritten character recognition (HCR) has changed dramatically, moving from manually created feature extraction methods to highly flexible data-driven models. This paper offers a thorough examination of the latest HCR techniques, highlighting the interaction between deep learning architectures like Transformers, Recurrent Neural Networks (RNNs), and Convolutional Neural Networks (CNNs) and image processing pipelines. The difficulties presented by different alphabets, writing styles, and morphological complications in scripts like Devanagari, Arabic, Chinese, and Latin are specifically addressed by cross-lingual and multi-script classification systems. The study groups current methods according to model designs, feature extraction tactics, preprocessing methods, and evaluation standards. It also draws attention to the present difficulties in attaining reliable performance on noisy datasets and low-resource languages. In order to provide information about the future direction of ubiquitous and script-agnostic HCR systems, this analysis concludes by outlining new trends and research gaps.