select_feature_set_trainf_knnT_select_feature_set_trainf_knnSelectFeatureSetTrainfKnnSelectFeatureSetTrainfKnnselect_feature_set_trainf_knn (Operator)

Name

select_feature_set_trainf_knnT_select_feature_set_trainf_knnSelectFeatureSetTrainfKnnSelectFeatureSetTrainfKnnselect_feature_set_trainf_knn — Select an optimal combination of features to classify OCR data.

Signature

select_feature_set_trainf_knn( : : TrainingFile, FeatureList, SelectionMethod, Width, Height, GenParamName, GenParamValue : OCRHandle, FeatureSet, Score)

Herror T_select_feature_set_trainf_knn(const Htuple TrainingFile, const Htuple FeatureList, const Htuple SelectionMethod, const Htuple Width, const Htuple Height, const Htuple GenParamName, const Htuple GenParamValue, Htuple* OCRHandle, Htuple* FeatureSet, Htuple* Score)

void SelectFeatureSetTrainfKnn(const HTuple& TrainingFile, const HTuple& FeatureList, const HTuple& SelectionMethod, const HTuple& Width, const HTuple& Height, const HTuple& GenParamName, const HTuple& GenParamValue, HTuple* OCRHandle, HTuple* FeatureSet, HTuple* Score)

HTuple HOCRKnn::SelectFeatureSetTrainfKnn(const HTuple& TrainingFile, const HTuple& FeatureList, const HString& SelectionMethod, Hlong Width, Hlong Height, const HTuple& GenParamName, const HTuple& GenParamValue, HTuple* Score)

HTuple HOCRKnn::SelectFeatureSetTrainfKnn(const HString& TrainingFile, const HString& FeatureList, const HString& SelectionMethod, Hlong Width, Hlong Height, const HTuple& GenParamName, const HTuple& GenParamValue, HTuple* Score)

HTuple HOCRKnn::SelectFeatureSetTrainfKnn(const char* TrainingFile, const char* FeatureList, const char* SelectionMethod, Hlong Width, Hlong Height, const HTuple& GenParamName, const HTuple& GenParamValue, HTuple* Score)

HTuple HOCRKnn::SelectFeatureSetTrainfKnn(const wchar_t* TrainingFile, const wchar_t* FeatureList, const wchar_t* SelectionMethod, Hlong Width, Hlong Height, const HTuple& GenParamName, const HTuple& GenParamValue, HTuple* Score)   (Windows only)

static void HOperatorSet.SelectFeatureSetTrainfKnn(HTuple trainingFile, HTuple featureList, HTuple selectionMethod, HTuple width, HTuple height, HTuple genParamName, HTuple genParamValue, out HTuple OCRHandle, out HTuple featureSet, out HTuple score)

HTuple HOCRKnn.SelectFeatureSetTrainfKnn(HTuple trainingFile, HTuple featureList, string selectionMethod, int width, int height, HTuple genParamName, HTuple genParamValue, out HTuple score)

HTuple HOCRKnn.SelectFeatureSetTrainfKnn(string trainingFile, string featureList, string selectionMethod, int width, int height, HTuple genParamName, HTuple genParamValue, out HTuple score)

def select_feature_set_trainf_knn(training_file: MaybeSequence[str], feature_list: MaybeSequence[str], selection_method: str, width: int, height: int, gen_param_name: Sequence[str], gen_param_value: Sequence[Union[int, str, float]]) -> Tuple[HHandle, Sequence[str], Sequence[float]]

Description

select_feature_set_trainf_knnselect_feature_set_trainf_knnSelectFeatureSetTrainfKnnSelectFeatureSetTrainfKnnSelectFeatureSetTrainfKnnselect_feature_set_trainf_knn selects an optimal combination of features, to classify the data given in the training file TrainingFileTrainingFileTrainingFileTrainingFiletrainingFiletraining_file with a k-Nearest Neighbor classifier, for details see create_ocr_class_knncreate_ocr_class_knnCreateOcrClassKnnCreateOcrClassKnnCreateOcrClassKnncreate_ocr_class_knn.

Possible features are all OCR features listed and explained in create_ocr_class_knncreate_ocr_class_knnCreateOcrClassKnnCreateOcrClassKnnCreateOcrClassKnncreate_ocr_class_knn. All candidates which should be tested can be specified in FeatureListFeatureListFeatureListFeatureListfeatureListfeature_list. A subset of these features is returned as selected features in FeatureSetFeatureSetFeatureSetFeatureSetfeatureSetfeature_set.

select_feature_set_trainf_knnselect_feature_set_trainf_knnSelectFeatureSetTrainfKnnSelectFeatureSetTrainfKnnSelectFeatureSetTrainfKnnselect_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_knnselect_feature_set_knnSelectFeatureSetKnnSelectFeatureSetKnnSelectFeatureSetKnnselect_feature_set_knn.

The selection method SelectionMethodSelectionMethodSelectionMethodSelectionMethodselectionMethodselection_method is either a greedy search 'greedy'"greedy""greedy""greedy""greedy""greedy" (iteratively add the feature with highest gain) or the dynamically oscillating search 'greedy_oscillating'"greedy_oscillating""greedy_oscillating""greedy_oscillating""greedy_oscillating""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'"greedy""greedy""greedy""greedy""greedy" is generally preferable, since it is faster. Only in cases when a large training set is available the method 'greedy_oscillating'"greedy_oscillating""greedy_oscillating""greedy_oscillating""greedy_oscillating""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 ScoreScoreScoreScorescorescore.

The k-NN classifier can be parametrized using the following values in GenParamNameGenParamNameGenParamNameGenParamNamegenParamNamegen_param_name and GenParamValueGenParamValueGenParamValueGenParamValuegenParamValuegen_param_value:

'num_neighbors'"num_neighbors""num_neighbors""num_neighbors""num_neighbors""num_neighbors":

The number of minimally evaluated nodes, increase this value for high dimensional data.

ValueSuggestion 1, 2, 5, 10

DefaultValue 1

'num_trees'"num_trees""num_trees""num_trees""num_trees""num_trees":

Number of search trees in the k-NN classifier

ValueSuggestion 1, 4, 10

DefaultValue 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_knnselect_feature_set_trainf_knnSelectFeatureSetTrainfKnnSelectFeatureSetTrainfKnnSelectFeatureSetTrainfKnnselect_feature_set_trainf_knn may deliver a classifier with a very high score. However, the classifier may perform poorly when tested.

Execution Information

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

TrainingFileTrainingFileTrainingFileTrainingFiletrainingFiletraining_file (input_control)  filename.read(-array) HTupleMaybeSequence[str]HTupleHtuple (string) (string) (HString) (char*)

Names of the training files.

Default value: '' "" "" "" "" ""

File extension: .trf, .otr

FeatureListFeatureListFeatureListFeatureListfeatureListfeature_list (input_control)  string(-array) HTupleMaybeSequence[str]HTupleHtuple (string) (string) (HString) (char*)

List of features that should be considered for selection.

Default value: ['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'] ["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"] ["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"] ["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"] ["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"] ["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'"anisometry""anisometry""anisometry""anisometry""anisometry", 'chord_histo'"chord_histo""chord_histo""chord_histo""chord_histo""chord_histo", 'compactness'"compactness""compactness""compactness""compactness""compactness", 'convexity'"convexity""convexity""convexity""convexity""convexity", 'cooc'"cooc""cooc""cooc""cooc""cooc", 'default'"default""default""default""default""default", 'foreground'"foreground""foreground""foreground""foreground""foreground", 'foreground_grid_16'"foreground_grid_16""foreground_grid_16""foreground_grid_16""foreground_grid_16""foreground_grid_16", 'foreground_grid_9'"foreground_grid_9""foreground_grid_9""foreground_grid_9""foreground_grid_9""foreground_grid_9", 'gradient_8dir'"gradient_8dir""gradient_8dir""gradient_8dir""gradient_8dir""gradient_8dir", 'height'"height""height""height""height""height", 'moments_central'"moments_central""moments_central""moments_central""moments_central""moments_central", 'moments_gray_plane'"moments_gray_plane""moments_gray_plane""moments_gray_plane""moments_gray_plane""moments_gray_plane", 'moments_region_2nd_invar'"moments_region_2nd_invar""moments_region_2nd_invar""moments_region_2nd_invar""moments_region_2nd_invar""moments_region_2nd_invar", 'moments_region_2nd_rel_invar'"moments_region_2nd_rel_invar""moments_region_2nd_rel_invar""moments_region_2nd_rel_invar""moments_region_2nd_rel_invar""moments_region_2nd_rel_invar", 'moments_region_3rd_invar'"moments_region_3rd_invar""moments_region_3rd_invar""moments_region_3rd_invar""moments_region_3rd_invar""moments_region_3rd_invar", 'num_connect'"num_connect""num_connect""num_connect""num_connect""num_connect", 'num_holes'"num_holes""num_holes""num_holes""num_holes""num_holes", 'num_runs'"num_runs""num_runs""num_runs""num_runs""num_runs", 'phi'"phi""phi""phi""phi""phi", 'pixel'"pixel""pixel""pixel""pixel""pixel", 'pixel_binary'"pixel_binary""pixel_binary""pixel_binary""pixel_binary""pixel_binary", 'pixel_invar'"pixel_invar""pixel_invar""pixel_invar""pixel_invar""pixel_invar", 'projection_horizontal'"projection_horizontal""projection_horizontal""projection_horizontal""projection_horizontal""projection_horizontal", 'projection_horizontal_invar'"projection_horizontal_invar""projection_horizontal_invar""projection_horizontal_invar""projection_horizontal_invar""projection_horizontal_invar", 'projection_vertical'"projection_vertical""projection_vertical""projection_vertical""projection_vertical""projection_vertical", 'projection_vertical_invar'"projection_vertical_invar""projection_vertical_invar""projection_vertical_invar""projection_vertical_invar""projection_vertical_invar", 'ratio'"ratio""ratio""ratio""ratio""ratio", 'width'"width""width""width""width""width", 'zoom_factor'"zoom_factor""zoom_factor""zoom_factor""zoom_factor""zoom_factor"

SelectionMethodSelectionMethodSelectionMethodSelectionMethodselectionMethodselection_method (input_control)  string HTuplestrHTupleHtuple (string) (string) (HString) (char*)

Method to perform the selection.

Default value: 'greedy' "greedy" "greedy" "greedy" "greedy" "greedy"

List of values: 'greedy'"greedy""greedy""greedy""greedy""greedy", 'greedy_oscillating'"greedy_oscillating""greedy_oscillating""greedy_oscillating""greedy_oscillating""greedy_oscillating"

WidthWidthWidthWidthwidthwidth (input_control)  integer HTupleintHTupleHtuple (integer) (int / long) (Hlong) (Hlong)

Width of the rectangle to which the gray values of the segmented character are zoomed.

Default value: 15

HeightHeightHeightHeightheightheight (input_control)  integer HTupleintHTupleHtuple (integer) (int / long) (Hlong) (Hlong)

Height of the rectangle to which the gray values of the segmented character are zoomed.

Default value: 16

GenParamNameGenParamNameGenParamNameGenParamNamegenParamNamegen_param_name (input_control)  string-array HTupleSequence[str]HTupleHtuple (string) (string) (HString) (char*)

Names of generic parameters to configure the selection process and the classifier.

Default value: []

List of values: 'num_neighbors'"num_neighbors""num_neighbors""num_neighbors""num_neighbors""num_neighbors"

GenParamValueGenParamValueGenParamValueGenParamValuegenParamValuegen_param_value (input_control)  number-array HTupleSequence[Union[int, str, float]]HTupleHtuple (real / integer / string) (double / int / long / string) (double / Hlong / HString) (double / Hlong / char*)

Values of generic parameters to configure the selection process and the classifier.

Default value: []

Suggested values: 1, 2, 3

OCRHandleOCRHandleOCRHandleOCRHandleOCRHandleocrhandle (output_control)  ocr_knn HOCRKnn, HTupleHHandleHTupleHtuple (handle) (IntPtr) (HHandle) (handle)

Trained OCR-k-NN classifier.

FeatureSetFeatureSetFeatureSetFeatureSetfeatureSetfeature_set (output_control)  string-array HTupleSequence[str]HTupleHtuple (string) (string) (HString) (char*)

Selected feature set, contains only entries from FeatureListFeatureListFeatureListFeatureListfeatureListfeature_list.

ScoreScoreScoreScorescorescore (output_control)  real-array HTupleSequence[float]HTupleHtuple (real) (double) (double) (double)

Achieved score using tow-fold cross-validation.

Result

If the parameters are valid, the operator select_feature_set_trainf_knnselect_feature_set_trainf_knnSelectFeatureSetTrainfKnnSelectFeatureSetTrainfKnnSelectFeatureSetTrainfKnnselect_feature_set_trainf_knn returns the value 2 (H_MSG_TRUE). If necessary, an exception is raised.

Alternatives

select_feature_set_trainf_svmselect_feature_set_trainf_svmSelectFeatureSetTrainfSvmSelectFeatureSetTrainfSvmSelectFeatureSetTrainfSvmselect_feature_set_trainf_svm, select_feature_set_trainf_mlpselect_feature_set_trainf_mlpSelectFeatureSetTrainfMlpSelectFeatureSetTrainfMlpSelectFeatureSetTrainfMlpselect_feature_set_trainf_mlp

See also

select_feature_set_knnselect_feature_set_knnSelectFeatureSetKnnSelectFeatureSetKnnSelectFeatureSetKnnselect_feature_set_knn

Module

OCR/OCV