Handwritten and Printed Text Segmentation via Region-Aware Human-Writing Descriptor Engineering

2026-07-17

AI Summary

This work addresses the challenge of accurately segmenting handwritten and printed text in document digitization under the computational constraints of edge devices, where existing deep learning approaches incur high computational costs and are difficult to deploy in lightweight settings. To overcome this, the authors propose a novel lightweight segmentation framework that operates without deep neural networks. The method first extracts semantically coherent text regions via sentence-level connected component segmentation, then introduces a region-aware handwriting descriptor (RHD) to effectively capture the variability inherent in handwriting. Finally, a conventional classifier is employed for efficient discrimination between handwritten and printed text. Evaluated on both the newly curated MAD-HPTS dataset and the public PHD-AS benchmark, the approach outperforms state-of-the-art methods, achieving over 8× inference speedup with only a 1.4% drop in accuracy, thereby substantially reducing computational overhead and enabling practical edge deployment.

Abstract

With the increasing demand for reusing paper documents in educational and office settings, accurate segmentation of handwritten and printed text has become a crucial step in document digitization. Although numerous deep learning models have been developed for this task, their high computational cost limits deployment on resource-constrained edge devices. To address this challenge, we present a lightweight framework optimized for efficient performance on devices with severely limited computational capacity. Our approach begins with the Sentence-level Connected Component Segmentation algorithm, aimed at extracting coherent sentence-level segments from document images. We then design a novel Region-aware Handwriting Descriptor (RHD) to capture the intrinsic variability of human handwriting at the sentence level. A simple conventional classifier can then be seamlessly integrated with our designed descriptor, demonstrating strong classification performance for distinguishing handwritten and printed sentence-level text images, highlighting that the proposed descriptor is agnostic to the choice of classifier. Extensive experiments are performed on our self-constructed Multilingual High-Quality Annotated Dataset for Handwritten and Printed Text Segmentation (MAD-HPTS) and a public benchmark PHD-AS, and the experimental results demonstrate that the proposed framework outperforms current state-of-the-art methods in both accuracy and computational efficiency. On MAD-HPTS, our method sacrifices only 1.4% accuracy compared to the leading deep neural network baseline, yet achieves more than 8 times speedup in inference, making it well-suited for lightweight deployment.

Problem

• text segmentation
• handwritten text
• printed text
• edge devices
• document digitization

Innovation

• Region-aware Handwriting Descriptor
• lightweight framework
• sentence-level segmentation
• handwritten-printed text separation
• edge deployment