The core aspiration of this proposed work is to classify Tamil characters inscribed in the palm leaf manuscript using an Artificial Neural Network. Tamil palm leaf manuscript characters in the form of images were processed and segmented using contour-based convex hull bounding box segmentation. The segmented characters were transformed into two forms: Binary Coded Value and the Gray-Level Co-occurrence Matrix (GLCM) feature. The features extracted from the segmented characters were trained by the proposed method of the Modified Adaptive Backpropagation Network (MABPN) algorithm with Shannon activation function. Weight initialization plays an important role in the Backpropagation Neural Network, and hence Nguyen-Widrow weight initialization was introduced to initialize the weights instead of random weight initialization in the proposed method. The models evaluated are MABPN with Shannon activation function using Nguyen-Widrow weight initialization in two forms of input: Binary Coded Value and GLCM feature extracted values. The proposed method with GLCM features as input gave a promising result over binary coded transform.

中文翻译：

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使用具有 GLCM 特征的改进自适应反向传播神经网络对棕榈叶铭文上的古代手写泰米尔语字符进行分类

这项拟议工作的核心愿望是使用人工神经网络对棕榈叶手稿中的泰米尔语字符进行分类。使用基于轮廓的凸包边界框分割对图像形式的泰米尔棕榈叶手稿字符进行处理和分割。分割的字符被转换成两种形式：二进制编码值和灰度共现矩阵（GLCM）特征。从分割字符中提取的特征通过改进的自适应反向传播网络（MABPN）算法与香农激活函数的方法进行训练。权重初始化在反向传播神经网络中起着重要作用，因此在所提出的方法中引入了 Nguyen-Widrow 权重初始化来初始化权重，而不是随机权重初始化。评估的模型是带有香农激活函数的 MABPN，在两种输入形式中使用 Nguyen-Widrow 权重初始化：二进制编码值和 GLCM 特征提取值。所提出的以 GLCM 特征作为输入的方法在二进制编码变换上给出了有希望的结果。