In recent data, Optical character recognition (OCR) systems have laid hands in the field of most popular language recognitions. Unlike other languages, the Tamil language is more complex to recognize, and hence considerable efforts have been laid in literature. However, the models are not yet well-organized for precise recognition of Tamil characters. Thus, the current research work develops a novel Tamil Handwritten Character Recognition approach by following two major processes, viz. pre-processing and recognition. The pre-processing phase encloses RGB to grayscale conversion, binarization with thresholding, image complementation, morphological operations, and linearization. Subsequently, the pre-processed image after linearization is subjected to recognition via an optimally configured Convolutional Neural Network (CNN). More particularly, the fully connected layer and weights are fine-tuned by a new Self Adaptive Lion Algorithm (SALA) that is the conceptual improvement of the standard Lion Algorithm (LA). The performance of the proposed work is compared and proved over other state-of-the-art models with respect to certain performance measures.

在最近的数据中，光学字符识别（OCR）系统已在大多数流行语言识别领域中占有一席之地。与其他语言不同，泰米尔语更难以识别，因此在文学上已付出了巨大的努力。但是，这些模型尚未很好地组织以精确识别泰米尔语字符。因此，当前的研究工作通过遵循以下两个主要过程开发了一种新颖的泰米尔语手写字符识别方法。预处理和识别。预处理阶段包括RGB到灰度的转换，带阈值的二值化，图像互补，形态运算和线性化。随后，经过优化配置的卷积神经网络（CNN）对线性化后的预处理图像进行识别。更具体地说，通过新的自适应Lion算法（SALA）对完全连接的层和权重进行了微调，这是对标准Lion算法（LA）的概念上的改进。在某些性能指标方面，与其他最新模型相比，对建议工作的性能进行了比较和证明。