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<p>Character recognition in ancient Tamil inscriptions poses significant challenges due to variations in writing styles, high inter-class similarity, and the complexity of compound characters. This study proposes a novel Weighted Average Deep Ensemble Learning model optimized using the Cuckoo Search Optimization algorithm (Meta-WADE) to address these issues. The model integrates three Deep Convolutional Neural Networks (DCNNs) and two transfer learning models, with their weights optimized via CSO to enhance performance. Extensive experiments on a large dataset of ancient Tamil characters demonstrate the model’s efficacy, achieving state-of-the-art results with an accuracy of 98.85%, precision of 97.11%, recall of 98.90%, and F1 score of 98.00%. Here we show that metaheuristic-based ensemble approaches can significantly improve the accuracy and robustness of ancient character recognition systems.</p>

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Optimizing deep ensemble learning for ancient Tamil inscription character recognition via metaheuristic weighting

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  • 出版模式：

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  • 发表日期：

    2026年4月20日

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  • A. Aswathy,
  • P. Uma Maheswari

摘要

Character recognition in ancient Tamil inscriptions poses significant challenges due to variations in writing styles, high inter-class similarity, and the complexity of compound characters. This study proposes a novel Weighted Average Deep Ensemble Learning model optimized using the Cuckoo Search Optimization algorithm (Meta-WADE) to address these issues. The model integrates three Deep Convolutional Neural Networks (DCNNs) and two transfer learning models, with their weights optimized via CSO to enhance performance. Extensive experiments on a large dataset of ancient Tamil characters demonstrate the model’s efficacy, achieving state-of-the-art results with an accuracy of 98.85%, precision of 97.11%, recall of 98.90%, and F1 score of 98.00%. Here we show that metaheuristic-based ensemble approaches can significantly improve the accuracy and robustness of ancient character recognition systems.