OCR Of Devanagari Script Using CNN

Sakshi. S. Sawant, Aparna. S. Shirkande, N. Shinde, Sharanya Rao
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Abstract

Devanagari script is widely used across India. It forms many languages like Hindi, Marathi, Nepali and Sanskrit languages. As the Devanagari characters are similar to the hindi character the national language of India. It is important to recognize the characters to understand the message that particular tries to tell. The automatic character recognition system is thus developing for the Devanagari script. The character recognition process converts an image of a character into machine-readable format also its English corresponds. In this paper, we are using Convolutional Neural Network for developing the character recognition system. Convolutional neural network learns directly from data. It is a type of Deep learning neural network architecture. CNN is useful as it does not require any human intervention and performs the identification of important features on its own. The proposed paper uses a CNN algorithm applied to a dataset of 49 characters of Devanagari script. The dataset contains of total 4018 Images. The algorithm of the Convolutional Neural Network is applied to train the dataset. The input image to be predicted is first preprocessed and then the model predicts the output result. The system is designed in Jupyter Lab using Python. The Convolutional Neural Network model's overall accuracy is 98%.
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使用CNN的Devanagari脚本的OCR
Devanagari文字在印度广泛使用。它形成了许多语言,如印地语、马拉地语、尼泊尔语和梵语。由于Devanagari字符与印度的国语印地语字符相似。重要的是要识别字符,以理解特定的信息试图告诉。因此,针对梵文的自动字符识别系统正在发展。字符识别过程将字符的图像转换为机器可读的格式,也将其转换为对应的英文格式。在本文中,我们使用卷积神经网络来开发字符识别系统。卷积神经网络直接从数据中学习。它是一种深度学习神经网络架构。CNN是有用的,因为它不需要任何人为干预,并自行执行重要特征的识别。本文将CNN算法应用于一个包含49个Devanagari文字的数据集。该数据集共包含4018张图片。采用卷积神经网络算法对数据集进行训练。首先对待预测的输入图像进行预处理,然后模型对输出结果进行预测。该系统是在Jupyter Lab中使用Python进行设计的。卷积神经网络模型的总体准确率为98%。
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