This paper provides a comprehensive survey of advancements and challenges in Handwritten Text Recognition (HTR), focusing on the digitization of historical documents, particularly the Belfort civil registers of births in French. The study highlights the unique characteristics of these documents, such as writing style variations, overlapped characters, and marginal annotations, which pose significant challenges for HTR systems. The paper reviews modern and historical HTR offline systems, classifying them based on techniques, datasets, publication years, and recognition levels. It also presents an analysis of system accuracies, showcasing the performance of commercial HTR systems and summarizing publicly available datasets, especially those used in ICDAR and ICFHR competitions. The survey covers recent advancements in HTR, including deep learning approaches like CNN, RNN, and CRNN, and discusses the importance of feature extraction and post-processing stages. The paper concludes with a discussion on current research directions and future directions in HTR, emphasizing the need for innovative solutions to overcome the challenges in transcribing hybrid-form documents.This paper provides a comprehensive survey of advancements and challenges in Handwritten Text Recognition (HTR), focusing on the digitization of historical documents, particularly the Belfort civil registers of births in French. The study highlights the unique characteristics of these documents, such as writing style variations, overlapped characters, and marginal annotations, which pose significant challenges for HTR systems. The paper reviews modern and historical HTR offline systems, classifying them based on techniques, datasets, publication years, and recognition levels. It also presents an analysis of system accuracies, showcasing the performance of commercial HTR systems and summarizing publicly available datasets, especially those used in ICDAR and ICFHR competitions. The survey covers recent advancements in HTR, including deep learning approaches like CNN, RNN, and CRNN, and discusses the importance of feature extraction and post-processing stages. The paper concludes with a discussion on current research directions and future directions in HTR, emphasizing the need for innovative solutions to overcome the challenges in transcribing hybrid-form documents.