Segmentation of handwritten words into isolated characters and their recognition are challenging due to the presence of high variability and cursiveness in Indian scripts. The complex shapes and availability of numerous atomic character classes, compound characters, modifiers, ascendants, and descendants make the recognition task even more difficult. A holistic approach effectively tackles such issues by avoiding the character-level segmentation and the earlier holistic methods have been mostly developed using multi-stage machine learning architecture. In this study, a deep convolutional neural network-based holistic method termed ‘H-WordNet’ is proposed for handwritten word recognition. The H-WordNet model includes merely four convolutional layers and one fully connected layer to effectively classify the word images', which lead to a significant reduction in parameters. The efficacy of different pooling operations with the proposed model is investigated. The main purpose of this study is to avoid the need for handcrafted feature extraction and obtain a more stable and generalised system for word recognition. The proposed model is evaluated using a standard handwritten Bangla word database (CMATERdb2.1.2), which contains 18000 Bangla word images of 120 different categories and it obtained a higher recognition accuracy of 96.17% when compared to recent state-of-the-art methods.

中文翻译：

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H-WordNet：一种用于手写单词识别的整体卷积神经网络方法

由于印度文字的高度可变性和草书性，将手写单词分割成孤立的字符并对其进行识别具有挑战性。许多原子字符类，复合字符，修饰符，上升和后代的复杂形状和可用性使识别任务更加困难。整体方法通过避免字符级分割有效地解决了此类问题，并且较早的整体方法主要是使用多阶段机器学习架构开发的。在这项研究中，提出了一种基于深度卷积神经网络的整体方法，称为“ H-WordNet”，用于手写单词识别。H-WordNet模型仅包含四个卷积层和一个完全连接的层，可以有效地对单词图像进行分类，从而导致参数显着减少。提出的模型研究了不同合并操作的效率。这项研究的主要目的是避免手工提取特征，并获得一个更稳定，更通用的单词识别系统。使用标准的手写孟加拉语单词数据库（CMATERdb2.1.2）对提出的模型进行了评估，该数据库包含18000种120种不同类别的孟加拉语单词图像，与最近的最新方法相比，它具有更高的识别精度，为96.17％。 。