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<p>The research community is actively working on character recognition for various languages, including Tamil, Arabic, Chinese, Telugu, and Malayalam. It is important to digitize texts so that large-scale documents can be saved, retrieved, and analysed efficiently. This serves as a foundation for digital preservation of cultural heritage by exploring Optical Character Recognition (OCR). Tamil Handwritten Character Recognition (THCR) is challenging as there are many letters in Tamil that have only a small style difference. To address this, we have proposed a Deep Inception Neural Network framework with residual connections for efficient Tamil handwritten character recognition (TamHNet). A non-linear bilateral filter technique is used to preprocess the handwritten images, and TamHNet is employed to classify the Tamil handwritten characters. For evaluation, the proposed system is trained using our own Tamil Isolated Character Dataset (TICD), which comprises isolated Tamil handwritten characters collected from multiple individuals with diverse writing styles. These characters are uploaded to Mendeley for future research and benchmarking. The novelty of TamHNet lies in the domain-adaptive fine-tuning of the Inception-ResNet architecture for THCR.The proposed TamHNet goes beyond traditional methods by systematically finding and selectively unfreezing learnable layers to optimize weights and biases in a targeted way. Fine-tuning helps the model accurately represent the distinctive structural and complex differences in Tamil script, which leads to better feature discrimination and recognition performance. It uses the Adam optimizer, which combines first-order (momentum) and second-order (adaptive learning rate) estimations. This allows the learning rates for each parameter change over time to helps the model to converge faster and deal with sparse gradients better to stay stable when faced with complicated, non-convex loss surfaces. In THCR, adaptive parameter tuning using Adam will improve the model ability to learn small details, which makes the training process faster and more accurate. This robust framework, with a fine-tuned architecture, achieved an impressive accuracy of 99.8%, outperforming other state-of-the-art algorithms.</p>

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Deep inception neural network with residual connections for Tamil handwritten character recognition

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月23日

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  • Hariharan Periyasamy,
  • Sasikaladevi Natarajan,
  • Rengarajan Amirtharajan

摘要

The research community is actively working on character recognition for various languages, including Tamil, Arabic, Chinese, Telugu, and Malayalam. It is important to digitize texts so that large-scale documents can be saved, retrieved, and analysed efficiently. This serves as a foundation for digital preservation of cultural heritage by exploring Optical Character Recognition (OCR). Tamil Handwritten Character Recognition (THCR) is challenging as there are many letters in Tamil that have only a small style difference. To address this, we have proposed a Deep Inception Neural Network framework with residual connections for efficient Tamil handwritten character recognition (TamHNet). A non-linear bilateral filter technique is used to preprocess the handwritten images, and TamHNet is employed to classify the Tamil handwritten characters. For evaluation, the proposed system is trained using our own Tamil Isolated Character Dataset (TICD), which comprises isolated Tamil handwritten characters collected from multiple individuals with diverse writing styles. These characters are uploaded to Mendeley for future research and benchmarking. The novelty of TamHNet lies in the domain-adaptive fine-tuning of the Inception-ResNet architecture for THCR.The proposed TamHNet goes beyond traditional methods by systematically finding and selectively unfreezing learnable layers to optimize weights and biases in a targeted way. Fine-tuning helps the model accurately represent the distinctive structural and complex differences in Tamil script, which leads to better feature discrimination and recognition performance. It uses the Adam optimizer, which combines first-order (momentum) and second-order (adaptive learning rate) estimations. This allows the learning rates for each parameter change over time to helps the model to converge faster and deal with sparse gradients better to stay stable when faced with complicated, non-convex loss surfaces. In THCR, adaptive parameter tuning using Adam will improve the model ability to learn small details, which makes the training process faster and more accurate. This robust framework, with a fine-tuned architecture, achieved an impressive accuracy of 99.8%, outperforming other state-of-the-art algorithms.