Abstract
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.
Abstract (translated)
手写成绩单的数字化面临巨大挑战，因为不同人的书写风格和此类文档（如成绩单）中复杂的表格结构。本工作提出了一种混合方法，结合使用OpenCV进行表格检测以及PaddleOCR识别连续的手写文本。OpenCV的图像处理能力能够有效检测行与列，从而使表格检测更加轻量级且准确。此外，还实施了YOLOv8和改良版YOLOv8，在检测到的表格结构中与PaddleOCR一起用于手写文本识别，进一步增强了系统的灵活性。 所提出的模型在我们定制的数据集上实现了高精度，该数据集旨在代表各种不同的书写风格和复杂的表格布局。实验结果表明，改良版YOLOv8达到了92.72%的准确率，优于PaddleOCR（91.37%）和原始YOLOv8模型（88.91%）。这种效率降低了手动操作的需求，使得该方法成为数字化学术及行政文档的一种实际且快速的解决方案。 这项研究为文档自动化领域，特别是手写文档的理解提供了可操作且可靠的方法，有助于扩展、增强并整合相关技术。
URL
https://arxiv.org/abs/2508.16295
