icdar17code

Implementation of: Christlein, Vincent; Gropp, Martin; Fiel, Stefan; Maier, Andreas: "Unsupervised Feature Learning for Writer Identification and Writer Retrieval", ICDAR 2017

Please note that the parts are stripped from a larger code basis where parts are in C++, Python and Lua, so I hope it still is running. I will revise it as soon as possible, please drop me a line if you have questions. The algorithm is actually also quite easy to implement, so everyone should be able to reproduce my results.

Steps (no overall script currently...):

• Extract SIFT decsriptors from the binarized ICDAR17 competition and at the same time extract 32x32 patches (eliminate doubles). This code is in C++ and will be added soon, I will also upload the extracted patches and descriptors.
• run the pipeline (run_pipeline.py) just for clustering the SIFT descriptors (or use clustering.py directly)
• run cluster_index.py to create the cluster-indices / apply ratio criterium etc.
• run ocvmb2tt.lua to convert them to torch tensors
• create labelTrain file and sample files which you need for the CNN training
• train the resnet with loadwriters_resnet_fb.lua (yeah the name is bad, I will rename it soon)
• use extractMultipleFeatures_resnet_fb.lua to extract the new resnet features
• use run_pipeline.py to run the pipeline.

The code is as is, no warranty, etc. Please cite our work if you use my code or make some effort in improving it...