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

摘要: Abstract 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 mood of writers. Therefore detection in their subsequent correction have become critical pre-processing steps for document analysis retrieval systems neutralize variability styles improve performance word character recognition systems. this paper, we present new methods that use two novel core-region techniques estimate both angles images. Also prepare multilingual datasets comprised real synthetic images, along with ground truth information related each word, address lack standard research. These Bangla, Devanagari English words code made publicly available. Extensive experimental results prove efficacy proposed compared contemporary state-of-the-art methods. Moreover, robust, efficient, easily implementable. (The available at: https://scholarworks.boisestate.edu/saipl/ )