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h2. Summary

| Purpose | Matchbox: Duplicate detection tool for digital document collections. |
| Homepage \\ | [] |
| Source Code Repository \\ | [] |
| License \\ | Open source \\ |
| Debian Package | [] |

h2. Description

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|--threads THREADS] [\--sdk SDK|--sdk SDK] [\--precluster PRECLUSTER|--precluster PRECLUSTER] [\--clahe CLAHE|--clahe CLAHE] [\--config CONFIG|--config CONFIG] [\--featdir FEATDIR|--featdir FEATDIR] [\--bowsize BOWSIZE|--bowsize BOWSIZE] [\--csv|--csv] [-v] dir _all,extract,compare,train,bowhist,clean_

h2. User Experiences

currently installed at Austrian National Library

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