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New benchmark assesses robustness of Tamil handwritten character recognition

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced uTHCD-C, a new benchmark designed to evaluate the robustness of Tamil handwritten character recognition systems against various corruptions. This benchmark, based on the uTHCD dataset, includes ten corruption types at five severity levels, with a focus on handwriting-specific stroke-thinning. Nine different convolutional and transformer-based architectures were tested, revealing that ConvNeXt and Swin Transformer models performed best in terms of corruption error, while ShuffleNet and MNASNet showed lower robustness. The study also analyzed the trade-offs between robustness and computational efficiency for these models. AI

IMPACT Establishes a standardized method for evaluating and improving the robustness of AI models in specialized character recognition tasks.

RANK_REASON The item describes a new benchmark and evaluation of models for a specific research task (handwritten character recognition). [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New benchmark assesses robustness of Tamil handwritten character recognition

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The item describes a new benchmark and evaluation of models for a specific research task (handwritten character recognition). [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Preethi S, Meeradevi T, Arun Prakash A ·

    uTHCD-C: A Corruption-Robustness Benchmark for Tamil Handwritten Character Recognition

    arXiv:2610.36850v1 Announce Type: new Abstract: Evaluation of Tamil handwritten character recognition (HCR) has largely focused on clean test images, and a standardized multi-corruption robustness benchmark for isolated Tamil HCR has not been established. We introduce uTHCD-C, a …

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