Core Region Detection for Off-Line Unconstrained Handwritten Latin Words Using Word Envelops

Zone extraction is acclaimed as a significant pre-processing step in handwriting analysis. This paper presents a new method for separating ascenders and descenders from an unconstrained handwritten word and identifying its core-region. The method estimates correct core-region for complexities like long horizontal strokes, skewed words, first letter capital, hill and dale writing, jumping baselines and words with long descender curves, cursive handwriting, calligraphic words, title case words, very short words as shown in Fig. 1. It extracts two envelops from the word image and selects sample points that constitute the core region envelop. The method is tested on CVL, ICDAR-2013, ICFHR-2012, and IAM benchmark datasets of handwritten words written by multiple writers. We also created our own dataset of 100 words authored by 2 writers comprising all the above mentioned handwriting complexities. Due to non-availability of the Ground Truth for core-region extraction we created it manually for all the datasets. Our work reports an accuracy of 90.16% for correctly identifying all the three zones on 17,100 Latin words written by 802 individuals. Promising results are obtained by our core-region detection method when compared with the current state of the art methods.