The document discusses a run length smoothing-based algorithm for document segmentation, primarily focusing on both manhattan and non-manhattan layouts. It outlines the challenges of automatic document processing and the need for effective threshold settings in smoothing algorithms, particularly for digitized and born-digital documents. The research highlights the significance of automatic threshold assessment to improve the algorithm's performance in identifying relevant document frames.

1. Università degli studi di Bari “Aldo Moro”
Dipartimento di Informatica
A Run Length Smoothing-Based Algorithm
for non-Manhattan Document Segmentation
S. Ferilli, F. Leuzzi, F. Rotella, F. Esposito
Via Orabona, 4 - 70126 Bari – Italy
{ferilli, esposito}@di.uniba.it
L.A.C.A.M. {fabio.leuzzi, fulvio.rotella}@uniba.it
http://lacam.di.uniba.it

2. Introduction
● Automatic document processing a hot topic
― Layout analysis a fundamental step
● Identification of frames (relevant components in the document)
● Performance can determine quality and feasibility of the whole process
● Two different…
● Kinds of sources: Digitized (scanned) vs. Natively digital documents
● Categories of layouts: Manhattan vs. Non-Manhattan
● Types of algorithms: Top-down vs. Bottom-up
● Run Length Smoothing Algorithm
● Manhattan Layout
● Other works exploit or try to improve the RLSA by setting its parameters
● Many works on Manhattan layout
― Top-down strategies
● Less works on non-Manhattan layout
― Bottom-up strategies
● The Manhattan assumption holds for many typeset documents, simplifies
document processing…BUT cannot be assumed in general

3. RLSO
Application to scanned images
RLSO (Run Length Smoothing with OR)
1) horizontal smoothing with threshold th, row by row
2) vertical smoothing with threshold tv, column by column
● logical OR of the images obtained in steps 1 and 2
th = 5
tv = 4
(AND)

4. RLSO
?
Application to scanned images

5. RLSO
Application to born-digital documents
● Set horizontal/vertical distance thresholds th/tv
● build a frame for each basic block
● H ={(dh, b’, b’’) | b’ and b’’ are horizontally adjacent basic blocks
and dh is the horizontal distance between them}
●for all (dh,1, b’h,1, b’’h,1) ∈ H s.t. dh,1 ≤ th merge the frames to which b’h,1, b’’h,1
belong
● V = {(dv, b’, b’’) | b’ and b’’ are vertically adjacent basic blocks
and dv is the vertical distance between them}
● for all (dv,1, b’h,1, b’’h,1) ∈ V s.t. dv,1 ≤ tv merge the frames to which b’h,1, b’’h,1 belong
Reference block
Adjacent blocks
Non-adjacent blocks
Horizontal distance
Vertical distance

6. RLSO
Application to born-digital documents

7. RLSO
● Run Length Smoothing algorithms based on thresholds
― Hard to properly set manually (Not typical human activity)
― Heuristic approaches (Ad hoc)
― Tampers the idea of automatic processing
― Fixed thresholds not suitable to documents with several different
spacings
Automatic assessment of RLSO thresholds

8. RLSO
Automatic threshold assessment
● Study of Run Lengths behavior Figure 1.
a fragment of
― Histogram very irregular scientific paper
● Peaks = most frequent spacings
● Peak clusters = equally spaced
components
― Hard to exploit by automatic
techniques
― Cumulative histograms more regular
― Bar b = runs larger or equal than
b H’(i) = ∑ j≥ i H(j)
● Monotonically decreasing
― Flat zones = lengths for which no
runs are present
● Scaled down to 10%
― Reduces variability

9. RLSO
Automatic threshold assessment
● Select threshold on flat zones
― Derivative a good indicator
● Slope = 0
● Discrete approximation on bar
b:
― Tolerance possible Figure 1-a.
● Slope = – 30
― Skip starting and trailing flat
zones
● Starting zone = missing small
b
run lengths
● Trailing zone = merge whole
content Figure 1-b.
● Iteration of technique on
previously smoothed image
― Finds progressively more
(Figure 1-a/1-b) successive application of RLSO with
spaced components automatic threshold assessment on Figure 1.

10. Sample Evaluation

11. Conclusions
● RLSO (Run Length Smoothing with OR) identifies runs of white pixel in the
document image and fill them with black pixels whenever they are shorter than a
given threshold
– Both Manhattan and Non-Manhattan Layout
– Version for natively digital documents
● Automatic thresholding effective on documents having
– single character size
– different spacings
● Good baseline towards more complex documents
– different character sizes
– graphics
● Current and future Work
– Stop criterion for iteration
– Clustering based on positioning and spacing