ABSTRACT Slope and slant correction of offline handwritten word images are two of the major pre-processing steps in document image processing, because these reduce the variations in writing, thereby make further processing of the same much easier. This paper presents novel slope and slant correction methods that are applied in three different script handwritten words namely Devanagari, Bangla and Roman. The language dependency and the computational complexity of state-of-the-art approaches towards the word level slope and slant correction are addressed here. A new technique for approximate core region detection is introduced here for skew detection and then linear regression is recursively applied to de-skew the word image. Whereas, in case of slant correction, a novel cost function over the vertical projection of de-skewed image is designed and optimized to fix the uniform slant angle of text words. A new benchmarked database is developed herein to evaluate the proposed methods both quantitatively and qualitatively. Comparison of the performances by our methods with some existing slope and slant correction methods reveals that our methods are more accurate and faster.

中文翻译：

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三字手写文字图像斜斜校正的新方法

摘要离线手写文字图像的倾斜和倾斜校正是文档图像处理中的两个主要预处理步骤，因为它们减少了书写的变化，从而使进一步处理变得更加容易。本文提出了新颖的斜率和斜率校正方法，适用于三种不同的手写体文字，即梵文、孟加拉语和罗马文。此处解决了针对词级斜率和倾斜校正的最新方法的语言依赖性和计算复杂性。这里引入了一种近似核心区域检测的新技术来进行倾斜检测，然后递归地应用线性回归来对单词图像进行去倾斜。而在倾斜校正的情况下，设计并优化了去倾斜图像垂直投影上的新成本函数，以固定文本单词的均匀倾斜角度。这里开发了一个新的基准数据库，以定量和定性地评估所提出的方法。我们的方法与一些现有的斜率和倾斜校正方法的性能比较表明我们的方法更准确和更快。