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.