select_feature_set_trainf_knn🔗
Short description🔗
select_feature_set_trainf_knn — Select an optimal combination of features to classify OCR data.
Signature🔗
select_feature_set_trainf_knn( filename.read TrainingFile, string FeatureList, string SelectionMethod, integer Width, integer Height, string GenParamName, number GenParamValue, out ocr_knn OCRHandle, out string FeatureSet, out real Score )
Description🔗
select_feature_set_trainf_knn selects an optimal combination of
features, to classify the data given in the training file
TrainingFile with a k-Nearest Neighbor classifier,
for details see create_ocr_class_knn.
Possible features are all OCR features listed and explained in
create_ocr_class_knn. All candidates which should be tested can be
specified in FeatureList. A subset of these features is
returned as selected features in FeatureSet.
select_feature_set_trainf_knn is specialized on OCR problems and
only supports the features in the list mentioned before.
In order to use other features, please use the more general operator
select_feature_set_knn.
The selection method
SelectionMethod is either a greedy search 'greedy'
(iteratively add the feature with highest gain)
or the dynamically oscillating search 'greedy_oscillating'
(add the feature with highest gain and test then if any of the already added
features can be left out without great loss).
The method 'greedy' is generally preferable, since it is faster.
Only in cases when a large training set is available
the method 'greedy_oscillating' might return better results.
The optimization criterion is the classification rate of a two-fold
cross-validation of the training data. The best achieved value
is returned in Score.
The k-NN classifier can be parametrized using the following values in
GenParamName and GenParamValue:
-
'num_neighbors': The number of minimally evaluated nodes, increase this value for high dimensional data.
Suggested values: 1, 2, 5, 10
Default: 1
-
'num_trees': Number of search trees in the k-NN classifier
Suggested values: 1, 4, 10
Default: 4
Attention🔗
This operator may take considerable time, depending on the size of the data set in the training file, and the number of features.
Please note, that this operator should not be called, if only a small
set of training data is available. Due to the risk of overfitting the
operator select_feature_set_trainf_knn may deliver a classifier with
a very high score. However, the classifier may perform poorly when tested.
Execution information🔗
Execution information
-
Multithreading type: reentrant (runs in parallel with non-exclusive operators).
-
Multithreading scope: global (may be called from any thread).
-
Automatically parallelized on internal data level.
This operator returns a handle. Note that the state of an instance of this handle type may be changed by specific operators even though the handle is used as an input parameter by those operators.
Parameters🔗
TrainingFile (input_control) filename.read(-array) → (string)
Names of the training files.
Default: ''
File extension: .trf, .otr
FeatureList (input_control) string(-array) → (string)
List of features that should be considered for selection.
Default: ['zoom_factor', 'ratio', 'width', 'height', 'foreground', 'foreground_grid_9', 'foreground_grid_16', 'anisometry', 'compactness', 'convexity', 'moments_region_2nd_invar', 'moments_region_2nd_rel_invar', 'moments_region_3rd_invar', 'moments_central', 'phi', 'num_connect', 'num_holes', 'projection_horizontal', 'projection_vertical', 'projection_horizontal_invar', 'projection_vertical_invar', 'chord_histo', 'num_runs', 'pixel', 'pixel_invar', 'pixel_binary', 'gradient_8dir', 'cooc', 'moments_gray_plane']
List of values: 'anisometry', 'chord_histo', 'compactness', 'convexity', 'cooc', 'default', 'foreground', 'foreground_grid_16', 'foreground_grid_9', 'gradient_8dir', 'height', 'moments_central', 'moments_gray_plane', 'moments_region_2nd_invar', 'moments_region_2nd_rel_invar', 'moments_region_3rd_invar', 'num_connect', 'num_holes', 'num_runs', 'phi', 'pixel', 'pixel_binary', 'pixel_invar', 'projection_horizontal', 'projection_horizontal_invar', 'projection_vertical', 'projection_vertical_invar', 'ratio', 'width', 'zoom_factor'
SelectionMethod (input_control) string → (string)
Method to perform the selection.
Default: 'greedy'
List of values: 'greedy', 'greedy_oscillating'
Width (input_control) integer → (integer)
Width of the rectangle to which the gray values of the segmented character are zoomed.
Default: 15
Height (input_control) integer → (integer)
Height of the rectangle to which the gray values of the segmented character are zoomed.
Default: 16
GenParamName (input_control) string-array → (string)
Names of generic parameters to configure the selection process and the classifier.
Default: []
List of values: 'num_neighbors'
GenParamValue (input_control) number-array → (real / integer / string)
Values of generic parameters to configure the selection process and the classifier.
Default: []
Suggested values: 1, 2, 3
OCRHandle (output_control) ocr_knn → (handle)
Trained OCR-k-NN classifier.
FeatureSet (output_control) string-array → (string)
Selected feature set, contains only entries from
FeatureList.
Score (output_control) real-array → (real)
Achieved score using tow-fold cross-validation.
Result🔗
If the parameters are valid, the operator
select_feature_set_trainf_knn returns the value 2 (H_MSG_TRUE). If necessary,
an exception is raised.
Combinations with other operators🔗
Module🔗
OCR/OCV