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Purpose Matchbox: Duplicate detection tool for digital document collections.
Source Code Repository
Open source
Debian Package


The Matchbox tool is responsible for finding duplicatre pairs in a collection of digital documents based on SIFT features and SSIM methods. Consequently the tool takes a collection path with associated parameters as input. Currently three scenarios are implemented. These are:

  • Duplicate search in one turn (parameter ‘all’)
  • Professional duplicate search (experienced user can execute particular step in ‘FindDuplicates’ workflow)
  • Quick check if two documents are duplicates (based on previous BoW dictionary).
    Further parameters that influence and adjust duplicate analysis are currently investigated.

Image processing method:

The image processing algorithm can be described in 4 steps:
1. Document feature extraction

  • Interest point detection (applying Scale Invariant Feature Transform (SIFT) keypoint extraction)
  • Derivation of local feature descriptors (invariant to geometrical or radiometrical distortions)

2. Learning visual dictionary

  • Clustering method applied to all SIFT descriptors of all images using k-means algorithm
  • Run over collection and collect local descriptors in a visual dictionary using Bag-Of-Words (BoW) algorithm

3. Create visual histogram for each image document
4. Detect similar images based on visual histogram and local descriptors. Evaluate similarity score – pair-wise comparison of corresponding keyword frequency histograms for all documents. Conduct structural similarity analysis applying Sturctural SIMilarity (SSIM) approach (1 means identical and 0 means very different)

  • Rotate
  • Scale
  • Mask
  • Overlaying


FindDuplicates script can be invoked from command line. For standard usage two parameters are required: path to the collection documents and ‘all’.
scape/pc-qa-matchbox/Python# python2.7 -h
usage: [-h] [\--threads THREADS] [\--sdk SDK] [\--precluster PRECLUSTER] [\--clahe CLAHE] [\--config CONFIG] [\--featdir FEATDIR] [\--bowsize BOWSIZE] [\--csv] [-v] dir all,extract,compare,train,bowhist,clean

User Experiences

currently installed at Austrian National Library

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