The digitization of handwritten marksheets presents huge challenges due to the different styles of handwriting and complex table structures in such documents like marksheets. This work introduces a hybrid method that integrates OpenCV for table detection and PaddleOCR for recognizing sequential handwritten text. The image processing capabilities of OpenCV efficiently detects rows and columns which enable computationally lightweight and accurate table detection. Additionally, YOLOv8 and Modified YOLOv8 are implemented for handwritten text recognition within the detected table structures alongside PaddleOCR which further enhance the system's versatility. The proposed model achieves high accuracy on our custom dataset which is designed to represent different and diverse handwriting styles and complex table layouts. Experimental results demonstrate that YOLOv8 Modified achieves an accuracy of 92.72 percent, outperforming PaddleOCR 91.37 percent and the YOLOv8 model 88.91 percent. This efficiency reduces the necessity for manual work which makes this a practical and fast solution for digitizing academic as well as administrative documents. This research serves the field of document automation, particularly handwritten document understanding, by providing operational and reliable methods to scale, enhance, and integrate the technologies involved.

翻译：手写成绩单的数字化面临着巨大挑战，这主要源于此类文档中多样化的书写风格和复杂的表格结构。本研究提出了一种混合方法，该方法整合了用于表格检测的OpenCV和用于识别连续手写文本的PaddleOCR。OpenCV的图像处理能力能够高效检测行与列，从而实现计算量轻且准确的表格检测。此外，在检测到的表格结构内部，除了使用PaddleOCR外，还实现了YOLOv8和改进型YOLOv8进行手写文本识别，这进一步增强了系统的多功能性。所提出的模型在我们自定义的数据集上取得了高准确率，该数据集旨在代表不同且多样的书写风格以及复杂的表格布局。实验结果表明，改进型YOLOv8的准确率达到92.72%，优于PaddleOCR的91.37%和YOLOv8模型的88.91%。这种高效性减少了对人工工作的需求，使其成为数字化学术及行政文档的一种实用且快速的解决方案。本研究通过提供可操作且可靠的方法来扩展、增强和整合相关技术，服务于文档自动化领域，特别是手写文档理解。