Line and word segmentation of handwritten text document by mid-point detection and gap trailing.

This paper presents the text line and word segmentation from unconstrained handwritten documents based on horizontal projection histogram (HPH) to detect mid-points and gap trailing between lines. The midpoints are estimated from the HPH for the first 100 to 200 columns of the whole document. Then, considering the mid-points, the gap is tracked between two consecutive lines from locally computed HPH for a block having k rows and j columns. The HPH block is examined for various cases to locate optimal rows that separate adjacent lines. The proposed method segments curve, touching and skew-lines and is robust to writing variation and language independent. Word segmentation is not treated as a separate problem and goes efficiently alongside the line segmentation. As the trailing of space between neighboring lines goes on, the vertical projection Histogram (VPH) of t columns is monitored between the above and below separator of a line and find the optimal word separator. The algorithm is evaluated on two isolated datasets of different languages (Meitei Mayek and English). Text-line and word segmentation on Meitei Mayek handwritten documents achieve 91.84% and 88.96% accuracy respectively. Similarly, the handwritten English document meets 94.18% and 87.73% accuracy for line and word segmentation. [ABSTRACT FROM AUTHOR]