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DevNet: An Efficient CNN Architecture for Handwritten Devanagari Character Recognition
International Journal of Pattern Recognition and Artificial Intelligence ( IF 1.5 ) Pub Date : 2019-11-29 , DOI: 10.1142/s0218001420520096 Riya Guha 1 , Nibaran Das 1 , Mahantapas Kundu 1 , Mita Nasipuri 1 , K. C. Santosh 2
International Journal of Pattern Recognition and Artificial Intelligence ( IF 1.5 ) Pub Date : 2019-11-29 , DOI: 10.1142/s0218001420520096 Riya Guha 1 , Nibaran Das 1 , Mahantapas Kundu 1 , Mita Nasipuri 1 , K. C. Santosh 2
Affiliation
The writing style is a unique characteristic of a human being as it varies from one person to another. Due to such diversity in writing style, handwritten character recognition (HCR) under the purview of pattern recognition is not trivial. Conventional methods used handcrafted features that required a-priori domain knowledge, which is always not feasible. In such a case, extracting features automatically could potentially attract more interests. For this, in the literature, convolutional neural network (CNN) has been a popular approach to extract features from the image data. However, state-of-the-art works do not provide a generic CNN model for character recognition, Devanagari script, for instance. Therefore, in this work, we first study several different CNN models on publicly available handwritten Devanagari characters and numerals datasets. This means that our study is primarily focusing on comparative study by taking trainable parameters, training time and memory consumption into account. Later, we propose and design DevNet, a modified CNN architecture that produced promising results, since computational complexity and memory space are our primary concerns in design.
中文翻译:
DevNet:用于手写梵文字符识别的高效 CNN 架构
写作风格是一个人的独特特征,因为它因人而异。由于书写风格的多样性,模式识别范围内的手写字符识别(HCR)并非微不足道。传统方法使用需要先验领域知识的手工特征,这总是不可行的。在这种情况下,自动提取特征可能会吸引更多的兴趣。为此,在文献中,卷积神经网络 (CNN) 一直是从图像数据中提取特征的流行方法。然而,最先进的作品没有提供用于字符识别的通用 CNN 模型,例如梵文脚本。因此,在这项工作中,我们首先在公开可用的手写梵文字符和数字数据集上研究几种不同的 CNN 模型。这意味着我们的研究主要侧重于比较研究,将可训练参数、训练时间和内存消耗考虑在内。后来,我们提出并设计了 DevNet,这是一种经过修改的 CNN 架构,产生了可喜的结果,因为计算复杂性和内存空间是我们在设计中的主要关注点。
更新日期:2019-11-29
中文翻译:
DevNet:用于手写梵文字符识别的高效 CNN 架构
写作风格是一个人的独特特征,因为它因人而异。由于书写风格的多样性,模式识别范围内的手写字符识别(HCR)并非微不足道。传统方法使用需要先验领域知识的手工特征,这总是不可行的。在这种情况下,自动提取特征可能会吸引更多的兴趣。为此,在文献中,卷积神经网络 (CNN) 一直是从图像数据中提取特征的流行方法。然而,最先进的作品没有提供用于字符识别的通用 CNN 模型,例如梵文脚本。因此,在这项工作中,我们首先在公开可用的手写梵文字符和数字数据集上研究几种不同的 CNN 模型。这意味着我们的研究主要侧重于比较研究,将可训练参数、训练时间和内存消耗考虑在内。后来,我们提出并设计了 DevNet,这是一种经过修改的 CNN 架构,产生了可喜的结果,因为计算复杂性和内存空间是我们在设计中的主要关注点。