Dataset for Normalization of Unconstrained Handwritten Words in Terms of Slope and Slant Correction

In handwritten text skew and slant are inevitably introduced, but to varying degrees depending on several factors, such as the writing style, speed and mood of the writer. Therefore skew and slant detection in offline handwritten text words and their subsequent correction have become the critical pre-processing steps in Document Analysis and Retrieval systems to neutralize the variability in writing styles to improve the performance of word and character recognition systems. In this paper, we present two new methods for the estimation of slope and slant angles of offline handwritten word images along with two novel core-region detection techniques for both skewed and non-skewed text words. Besides this, we also prepare multilingual datasets comprising both real and synthetic handwritten word images along with ground truth information related to slope and slant of the same to address the lack of standard datasets in this regard. These datasets are made publicly available as word-level slope and slant datasets are scarce, especially words written in Bangla and Devanagari. Extensive experimental results prove the efficiency of the proposed methods compared to contemporary state-of-the-art methods. Moreover, the method is robust, efficient, and easily implementable.