Segmentation of heterogeneous document images : an ... - Tel

• Maximum right indentation of the text lines in the middle of the paragraph.
• Minimum right indentation of the text lines in the middle of the paragraph.
• Average vertical distance between text lines.
• Average height of text lines.
• Standard deviation of height for text lines.
• Average width of text lines.
• Standard deviation of width fro text lines.
• Average size of connected components.
• Standard deviation of size of connected components.
tel-00912566, version 1 - 2 Dec 2013
• Number of connected components.
• Paragraph’s width.
6.4 State decoding
State decoding is a procedure that determine the state of each node given their
weights. Two possible states are defined for each node: ”Preserve” or ”Remove”.
If a node takes the ”Preserve” state, that node and all its children should be
grouped as one paragraph. On the other hand a ”Remove” state indicates that
the two children of that node should not be grouped as one paragraph. Leaves
of the tree are single text lines and should always be preserved. The algorithm
is described in algorithm 1. W is the weight vector that should be trained.
6.5 Training method
The training method for our paragraph model is very similar to Collin’s voted
perceptron in regard to computing the direction of gradients. The major difference
is that the values of the features are not available beforehand and should
be computed online while training. For each document image in our trainset,
we start from the leaves of the tree where we already know the values of the features.
Then we move upward toward the root of the tree. The implementation
can be done using dynamic programming where we appoint to the root and the
root ask for feature values from its children. The pseudo code for computing ∆
values is provided in algorithm 2. In each iteration we initialize ∆ to zero and
call the procedure for the root node of the binary tree for first document. Then,
we continue calling procedures for all other documents in our training dataset.
After one passage for all documents, ∆ should be divided by the total number of
processed nodes in the whole trainset. Then each element of ∆ vector should be
added to the current weight vector and the next iteration the procedure starts
over with the new weights and we continue until convergence.
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