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

In offline handwritten text slope (or skew) and slant are inevitably introduced, but to varying degrees depending on several factors, such as the writing style, speed and mood of the writers. Therefore slope and slant detection in offline handwritten text and their subsequent correction have become the critical preprocessing steps for document analysis and retrieval systems to neutralize the variability of writing styles and to improve the performance of word and character recognition systems. In this paper, we present new methods that use two novel core-region detection techniques to estimate both the slope and slant angles of offline handwritten word images. Also we prepare multilingual datasets comprised of both real and synthetic handwritten word images, along with ground truth information related to the slope and slant of each word, to address the lack of standard datasets for this research. These datasets of Bangla, Devanagari and English words are made publicly available. Extensive experimental results prove the efficacy of the proposed methods compared to contemporary state-of-the-art methods. Moreover, the methods are robust, efficient, and easily implementable.