Text line Segmentation in Compressed Representation of Handwritten Document using Tunneling Algorithm. (arXiv:1901.11477v1 [cs.CV])

In this research work, we perform text line segmentation directly in
compressed representation of an unconstrained handwritten document image. In
this relation, we make use of text line terminal points which is the current
state-of-the-art. The terminal points spotted along both margins (left and
right) of a document image for every text line are considered as source and
target respectively. The tunneling algorithm uses a single agent (or robot) to
identify the coordinate positions in the compressed representation to perform
text-line segmentation of the document. The agent starts at a source point and
progressively tunnels a path routing in between two adjacent text lines and
reaches the probable target. The agent’s navigation path from source to the
target bypassing obstacles, if any, results in segregating the two adjacent
text lines. However, the target point would be known only when the agent
reaches the destination; this is applicable for all source points and
henceforth we could analyze the correspondence between source and target nodes.
Artificial Intelligence in Expert systems, dynamic programming and greedy
strategies are employed for every search space while tunneling. An exhaustive
experimentation is carried out on various benchmark datasets including ICDAR13
and the performances are reported.

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