Recognition of Bangla text from scene images through perspective correction

R. Ghoshal, A. Roy, S. Parui
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引用次数: 14

Abstract

This article proposes a scheme for automatic extraction and recognition of Bangla text from natural scene images. An image, when captured by a digital camera may have perspective distortion. Before extracting text symbols, this distortion is corrected using Homography transform. For text extraction, headlines are detected using morphology. First, the components attached or close to the detected headlines, are separated. Further, by applying certain shape and position based conditions we could distinguish text and non-text. Afterwards, by removing the headline we partition the text into two different zones. For recognition purpose, the local chain code histograms of input character are used as features. Finally, separate Multilayer perceptrons (MLPs) are used to recognize text symbols reside in different zones. The classifiers are trained using about 7500 samples of 53 classes. We tested our algorithm on 100 scene images.
通过透视校正从场景图像中识别孟加拉语文本
本文提出了一种从自然场景图像中自动提取和识别孟加拉语文本的方案。数码相机拍摄的图像可能有透视失真。在提取文本符号之前,使用同形变换对这种畸变进行校正。对于文本提取,使用形态学检测标题。首先,将附加或靠近检测到的标题的组件分开。此外,通过应用某些基于形状和位置的条件,我们可以区分文本和非文本。然后,通过删除标题,我们将文本划分为两个不同的区域。为了便于识别,使用输入字符的局部链码直方图作为特征。最后,使用单独的多层感知器(mlp)来识别驻留在不同区域的文本符号。分类器使用53个类别的约7500个样本进行训练。我们在100张场景图像上测试了算法。
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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