# Edge-Native Digitization of Handwritten Marksheets: A Hybrid Heuristic-Deep Learning Framework

arXiv: https://arxiv.org/abs/2508.16295 · HTML: https://mlsanity.com/paper/2508.16295/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 16 references that are in the map.
- First posted on arXiv 22 Aug 2025.

## Builds on (most cited first, 9 of 9)

- [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017)
- [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://mlsanity.com/paper/2109.10282/) (2021)
- [PP-OCR: A Practical Ultra Lightweight OCR System](https://mlsanity.com/paper/2009.09941/) (2020)
- [PubTables-1M: Towards comprehensive table extraction from unstructured documents](https://mlsanity.com/paper/2110.00061/) (2021)
- [DocLayout-YOLO: Enhancing Document Layout Analysis through Diverse Synthetic Data and Global-to-Local Adaptive Perception](https://mlsanity.com/paper/2410.12628/) (2024)
- [TableNet: Deep Learning model for end-to-end Table detection and Tabular data extraction from Scanned Document Images](https://mlsanity.com/paper/2001.01469/) (2020)
- [Deep learning for table detection and structure recognition: A survey](https://mlsanity.com/paper/2211.08469/) (2022)
- [WaveMix: Resource-efficient Token Mixing for Images](https://mlsanity.com/paper/2203.03689/) (2022)
- [EDEN: Evolutionary Deep Networks for Efficient Machine Learning](https://mlsanity.com/paper/1709.09161/) (2017)

## Abstract (arXiv)

The digitization of structured handwritten documents, such as academic marksheets, remains a significant challenge due to the dual complexity of irregular table structures and diverse handwriting styles. While recent Transformer-based approaches like TableNet and TrOCR achieve state-of-the-art accuracy, their high computational cost renders them unsuitable for resource-constrained edge deployments. This paper introduces a resource-efficient hybrid framework that integrates a heuristic OpenCV-based pipeline for rapid table structure detection with a modified lightweight YOLOv8 architecture for handwritten character recognition. By strategically removing the SPPF and deep C2f layers from the standard YOLOv8 backbone, we reduce computational overhead while maintaining high recognition fidelity. Experimental results on the EMNIST digit benchmark demonstrate that our Modified YOLOv8 model achieves 97.5% accuracy. Furthermore, we provide a comprehensive efficiency analysis showing that our framework offers a 95 times inference speedup over standard OCR pipelines and massive efficiency gains over emerging Large Multimodal Models (LMMs) like Qwen2.5-VL, achieving real-time performance 29 FPS on standard CPU hardware. A qualitative and quantitative evaluation on the AMES dataset, a challenging subset of real-world marksheets, confirms the system's robustness in handling mixed alphanumeric content, bridging the gap between high-performance deep learning and practical, scalable document automation.