CROHME_2014 | 2014 Lei Hu , Kenny Davila

CROHME_2014 is a Python library. CROHME_2014 has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. However CROHME_2014 build file is not available. You can download it from GitHub.

Copyright (c) 2013-2014 Lei Hu, Kenny Davila, Francisco Alvaro, Richard Zanibbi. DPRL CROHME 2014 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. DPRL CROHME 2014 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 DPRL CROHME 2014. If not, see This document is about DPRL's submission for the CROHME 2014. CROHME is the abbreviation of Competition on Recognition of Online Handwritten Mathematical Expression. The handwritten mathematical expression is preprocessed and rendered to an image. Symbol segmentation considers strokes in time series, using a binary AdaBoost classifier to determine which stroke pairs to merge. Then for each stroke, we compute three kinds of shape context features (stroke pair, local neighborhood and global shape contexts) with different scales, 21 stroke pair geometric features and symbol classification scores for the current stroke and stroke pair. The stroke pair shape context features covers the current stroke and the following stroke in time series. The local neighborhood shape context features includes the current stroke and its three nearest neighbor strokes in distance while the global shape context features covers the expression. Principal component analysis (PCA) is used for dimensionality reduction. The details of the segmentation can be found in the paper Segmenting Handwritten Math Symbols Using AdaBoost and Multi-Scale Shape Context Features. The symbol classifier uses a SVM with Gaussian Kernel trained for probabilistic classification for the symbol classification task. The feature vector used to describe each symbol contains a combination of general on-line features with some adaptations of off-line features. The on-line features used include the normalized length of the lines, number of traces, covariance matrix of the point coordinates, number of points with high variation in curvature and total angular variation used to draw each symbol. The off-line features include: normalized aspect ratio; the count, position of the first and position of the last times that traces intersect a set of lines at fixed horizontal and vertical positions (crossings); 2D fuzzy histograms of points and fuzzy histograms of orientations of the lines. All symbols are pre-processed in order to smooth the traces and reduce the amount of noise present on the symbol. More details about the symbol classifier can be found in the paper Using Off-line Features and Synthetic Data for On-line Handwritten Math Symbol Recognition. The parser recursively: 1) groups vertical structures (e.g. fractions, summations and square roots), 2) extracts the dominant operator (e.g. fraction line) in each vertical group, and then 3) locates symbols on the main baseline, and on the main baselines in superscripted and subscripted regions by finding an MST defined over candidate symbol pairs with their associated classes and spatial relationship, and then 4) repeats the procedure in nested regions of vertical structures (e.g. fraction numerators and denominators).