Bangla Handwritten Word Recognition System Using Convolutional Neural Network

Md. Tanvir Hossain, Md. Wahid Hasan, A. Das
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引用次数: 7

Abstract

In recent years, Machine Learning and Data Mining based research become prevalent and handwritten recognition is one of the hotcakes. Bangla handwritten word recognition and extraction acquired huge attention in many research sectors like Computer Vision, Image Processing, Machine Learning, and many others for a large field of applications. To tackle this challenging problem, a perfect segmentation and recognition method are described in this paper with a good percentage of accuracy. The main challenge was to introduce a sound segmentation system and merge multi-zoned characters. This paper proposes a multi-zoned character segmentation, and a merging method is also proposed, which can produce the handwritten term. Utilizing Convolutional Neural Network (CNN) for preparing 84% precision is accomplished for character level, and 82% precision is achieved in word level.
基于卷积神经网络的孟加拉语手写词识别系统
近年来,基于机器学习和数据挖掘的研究越来越流行,手写识别是其中的一个热点。孟加拉语手写词的识别和提取在计算机视觉、图像处理、机器学习等许多研究领域都得到了广泛的关注。为了解决这一具有挑战性的问题,本文描述了一种具有良好准确率的完美分割和识别方法。主要的挑战是引入一个健全的分割系统和合并多分区字符。本文提出了一种多分区字符分割方法,并提出了一种合并方法,可以产生手写词。利用卷积神经网络(CNN)进行预处理,字符级精度达到84%,词级精度达到82%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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