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Latest commit 21a6867 Apr 2, 2016 @kdavila kdavila -> corrected small issue in dataset_info.py tool that would crash if …
…the size

   of the largest class in dataset was multiple of number of bins.

README.md

DPRL Math Symbol Recognizers

Copyright (c) 2012-2014 Kenny Davila, Richard Zanibbi


RIT DPRL Math Symbol Recognizers is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

RIT DPRL Math Symbol Recognizers is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with RIT DPRL Math Symbol Recognizers. If not, see http://www.gnu.org/licenses/.

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The system divides is composed of different tools for data extraction, training and evaluation and other miscellaneous tools for isolated math symbol recognition.

A README file is included in the doc/ directory for each tool that describes its purpose, how to use it and what its parameters are.

A README file is included in the doc/ directory for each tool that describes its purpose, how to use it and what its parameters are.

The executable scripts on this release are the following:

  • Preprocessing of data: apply_PCA_parameters.py correct_labels.py get_enhanced_clustered_set.py get_PCA_parameters.py get_training_set.py
  • Analysis of datasets: count_common.py
    dataset_info.py extract_symbol.py

  • Training a symbol classifier: random_forest_classify.py svm_lin_classifier.py svm_rbf_classifier.py train_adaboost.py train_c45.py

  • Tools for evaluation boosted_test.py parallel_evaluate.py parallel_prob_evaluate.py

SOURCE FILES

Source code (in Python and C) is provided in the src/ directory.