A Run Length Smoothing-Based Algorithm for Non-Manhattan Document Segmentation
- 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.
- 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