Multi-skew detection of Indian script documents

There tire many documents where text lines are not parallel to each other i.e. these lines have different inclinations with the horizontal lines (mufti-skein documents). For the OCR of such a document we have to estimate the skew angle of individual text lines because a single rotation cannot de-skew all text lines of the document. In this paper, we describe a robust technique for multi-skew angle detection from Indian documents containing the most popular Indian scripts Devnagari and Bangla. Most characters in these scripts have horizontal lines at the top, called headlines. The character head-lines usually connect one another in a word and the word appears as a single component. In the proposed method, the connected components are tit,first labeled and selected. The upper envelopes of selected components tire found by column-wise scanning,from the top of the component. Portions of the zipper envelope satisfying the properties of a digital straight line tire detected. They arc then clustered into groups belonging to single text lines. Estimates from these individual clusters give the skew angle of each text line. The proposed mufti-skein detection technique has an accuracy about 98.3%.