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

arXiv: https://arxiv.org/abs/2607.15936 · HTML: https://mlsanity.com/paper/2607.15936/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 49 references that are in the map.
- First posted on arXiv 17 Jul 2026.

## Builds on (most cited first, 4 of 4)

- [Handwritten Text Segmentation via End-to-End Learning of Convolutional Neural Network](https://mlsanity.com/paper/1906.05229/) (2019)
- [Handwritten and Printed Text Segmentation: A Signature Case Study](https://mlsanity.com/paper/2307.07887/) (2023)
- [Statistical Texture Features based Handwritten and Printed Text Classification in South Indian Documents](https://mlsanity.com/paper/1303.3087/) (2013)
- [Handwritten and Printed Text Separation in Real Document](https://mlsanity.com/paper/1303.4614/) (2013)

## Abstract (arXiv)

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.