Lightweight framework efficiently segments handwritten and printed text

Researchers have developed a new lightweight framework for segmenting handwritten and printed text in document digitization. This approach utilizes a Sentence-level Connected Component Segmentation algorithm and a novel Region-aware Handwriting Descriptor (RHD) to efficiently capture handwriting variations. The method demonstrates strong performance, achieving over 8 times speedup in inference compared to deep neural network baselines while sacrificing only a small percentage of accuracy, making it suitable for resource-constrained devices. AI

IMPACT Enables efficient document digitization on resource-constrained devices.

RANK_REASON The cluster contains a research paper detailing a new algorithm and dataset. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• arXiv
• Multilingual High-Quality Annotated Dataset for Handwritten and Printed Text Segmentation (MAD-HPTS)
• Region-aware Handwriting Descriptor (RHD)
• Sentence-level Connected Component Segmentation algorithm

• paper
• other

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