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Optimally configured convolutional neural network for Tamil Handwritten Character Recognition by improved lion optimization model

机译：改进狮子优化模型的最佳配置的泰米尔手写字符识别卷积神经网络

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摘要

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.
机译：在最近的数据中，光学字符识别（OCR）系统已经奠定了最流行语言识别的领域。与其他语言不同，泰米尔语言更复杂，以识别，因此在文献中占据了相当大的努力。但是，对于精确识别泰米尔人物，模型尚未良好。因此，目前的研究工作通过遵循两个主要过程，viz开发了一种新颖的泰米尔手写字符识别方法。预处理和识别。预处理阶段附带RGB以灰度转换，二值化，具有阈值，图像互补，形态学操作和线性化。随后，经由最佳地配置的卷积神经网络（CNN）进行线性化之后的预处理图像。更具体地，通过新的自适应狮子算法（SALA）进行完全连接的层和权重，这是标准狮子算法（LA）的概念改进。比较拟议的工作的表现，并在其他最先进的模型上得到了某些绩效措施。

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