Datech2014-Session1-Document Representation Refinement for Precise Region Description
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Datech2014-Session1-Document Representation Refinement for Precise Region Description

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Slides of the presentation of the paper Document Representation Refinement for Precise Region Description by Christian Clausner, Stefan Pletschacher and Apostolos Antonacopoulos. #digidays

Slides of the presentation of the paper Document Representation Refinement for Precise Region Description by Christian Clausner, Stefan Pletschacher and Apostolos Antonacopoulos. #digidays

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Datech2014-Session1-Document Representation Refinement for Precise Region Description Datech2014-Session1-Document Representation Refinement for Precise Region Description Presentation Transcript

  • Document Representation Refinement for Precise Region Description Christian Clausner, Stefan Pletschacher and Apostolos Antonacopoulos PRImA Lab, School of Computing, Science and Engineering, University of Salford, United Kingdom
  • Document Page Regions DATeCH 2014 2 Segmentation, Classification • Region (block, zone): Connected area of a document image with content of a single specific type • Examples: Text, graphic, table
  • Region Representation • By geometric objects – Bounding box – Stack of rectangles – Polygon • By pixels – Bitmap – Run-length encoding DATeCH 2014 3
  • Need for Precise Region Descriptions • Precise description is crucial for all but the most trivial document analysis and recognition applications • For performance evaluation: The loss of quality introduced by imprecise regions can be bigger than the variation of accuracy of the actual recognition method DATeCH 2014 4
  • The Situation • Trend to more precise descriptions, but… • Output of state-of-the-artOCR systems: – Stacks of rectangles (ABBYY FineReader Engine 11) – Bounding boxes (Tesseract OCR 3.02) • Popular formats for layout analysis and OCR results: – ALTO XML (boxes, ellipses, polygons (region level only)) – FineReader XML (stacks of rectangles (region level only)) – PAGE XML (polygons for all levels) – HOCR (boxes) DATeCH 2014 5
  • Refinement through Polygonal Fitting • Applicable to regions that have child objects in the document model • A typical object hierarchy contains regions, text lines, words and glyphs (characters) • Idea: Tightly wrap a polygon around the child objects DATeCH 2014 6
  • Polygonal Fitting Approach 1. Create bitmasks for the child objects and transfer them to an empty bitmap 2. Fill the gaps between the child objects by a smearing approach 3. Optional: Exclude neighbour regions 4. Trace the contour of the foreground and create a polygon DATeCH 2014 7
  • 1 - Transferring Child Object to Bitmap • Starting point: Polygonal object (e.g. text line, word, or glyph) • Lossless conversion to rectangle based interval representation • Transferring the rectangles to the target bitmap DATeCH 2014 8
  • 2 – Smearing Approach • Goal: Connect all foreground components in the bitmap by filling the gaps in-between 1. Alternatingly fill horizontal and vertical gaps if they are smaller than a dynamic threshold (threshold is increased after each iteration) 2. If necessary, use diagonal smearing to connect remaining components DATeCH 2014 9
  • 3 – Subtraction of Neighbours • Optional step to avoid overlap with adjacent regions • Simply erase the corresponding pixels from the created bitmap DATeCH 2014 10
  • 4 – Outline Tracing • Trace the contour of the foreground component in the created bitmap • Create polygon on-the- fly by adding points for each change of direction (corner) DATeCH 2014 11
  • Experiments • Carried out on a dataset of contemporary documents consisting of scanned magazine and technical article pages • Processed with Tesseract OCR 3.02 (open source) • Exported to PAGE XML with and without refinement DATeCH 2014 12
  • DATeCH 2014 13 Original (unrefined) Refined
  • Results • Measurement of region overlaps (number and area) DATeCH 2014 14 Overlapping Regions Overlap Area (Megapixel) Original Outlines 621 (45.8%) 19.9 Refined Outlines 286 (21.1%) 2.5
  • Impact on Performance Evaluation • Real-world scenario • Measure the performance of Tesseract OCR engine • Evaluation metrics of previous ICDAR page segmentation competitions DATeCH 2014 15 Average success rate using originaloutlines 81.1% Average success rate using refined outlines 84.5% Average improvementfor all documents 3.4% Maximumimprovement 22.9%
  • Conclusion • Existing geometric region data can be significantly refined by fitting precise polygons around child objects • Validity and impact on real-world scenarios has been shown • Refinement in performance evaluation helps to eliminate problems that arise from insufficient geometric descriptions → Concentrate on real issues of OCR methods • Positive effect on accuracy of presentation/repurposing systems (highlighting, cropping, article tracking, etc.) • Approach used in Aletheia ground truth editor and result viewer (primaresearch.org/tools) DATeCH 2014 16
  • DATeCH 2014 17