create_ocr_class_svmT_create_ocr_class_svmCreateOcrClassSvmCreateOcrClassSvmcreate_ocr_class_svm (算子)

名称

create_ocr_class_svmT_create_ocr_class_svmCreateOcrClassSvmCreateOcrClassSvmcreate_ocr_class_svm — 使用支持向量机创建 OCR 分类器。

签名

描述

create_ocr_class_svmcreate_ocr_class_svmCreateOcrClassSvmCreateOcrClassSvmCreateOcrClassSvmcreate_ocr_class_svm creates an OCR classifier that uses a support vector machine (SVM). The handle of the OCR classifier is returned in OCRHandleOCRHandleOCRHandleOCRHandleOCRHandleocrhandle。

For a description on how an SVM works, see create_class_svmcreate_class_svmCreateClassSvmCreateClassSvmCreateClassSvmcreate_class_svm。create_ocr_class_svmcreate_ocr_class_svmCreateOcrClassSvmCreateOcrClassSvmCreateOcrClassSvmcreate_ocr_class_svm creates an SVM for classification with the classification mode given by ModeModeModeModemodemode. The length of the feature vector of the SVM (NumFeaturesNumFeaturesNumFeaturesNumFeaturesnumFeaturesnum_features in create_class_svmcreate_class_svmCreateClassSvmCreateClassSvmCreateClassSvmcreate_class_svm) is determined from the features that are used for the OCR, which are passed in FeaturesFeaturesFeaturesFeaturesfeaturesfeatures. The features are described below. The kernel is parametrized with KernelTypeKernelTypeKernelTypeKernelTypekernelTypekernel_type, KernelParamKernelParamKernelParamKernelParamkernelParamkernel_param and NuNuNuNununu like in create_class_svmcreate_class_svmCreateClassSvmCreateClassSvmCreateClassSvmcreate_class_svm。The number of classes of the SVM (NumClassesNumClassesNumClassesNumClassesnumClassesnum_classes in create_class_svmcreate_class_svmCreateClassSvmCreateClassSvmCreateClassSvmcreate_class_svm) is determined from the names of the characters to be used in the OCR, which are passed in CharactersCharactersCharactersCharacterscharacterscharacters. As described with create_class_svmcreate_class_svmCreateClassSvmCreateClassSvmCreateClassSvmcreate_class_svm, the parameters PreprocessingPreprocessingPreprocessingPreprocessingpreprocessingpreprocessing and NumComponentsNumComponentsNumComponentsNumComponentsnumComponentsnum_components can be used to specify a preprocessing of the data (i.e., the feature vectors). For the sake of numerical stability, PreprocessingPreprocessingPreprocessingPreprocessingpreprocessingpreprocessing can typically be set to 'normalization'"normalization""normalization""normalization""normalization""normalization". In order to speed up classification time, 'principal_components'"principal_components""principal_components""principal_components""principal_components""principal_components" or 'canonical_variates'"canonical_variates""canonical_variates""canonical_variates""canonical_variates""canonical_variates" can be used, as the number of input features can be significantly reduced without deterioration of the recognition rate.

The features to be used for the classification are determined by FeaturesFeaturesFeaturesFeaturesfeaturesfeatures. FeaturesFeaturesFeaturesFeaturesfeaturesfeatures can contain a tuple of feature names. Each of these feature names results in one or more features to be calculated for the classifier. Some of the feature names compute gray value features (e.g., 'pixel_invar'"pixel_invar""pixel_invar""pixel_invar""pixel_invar""pixel_invar"). Because a classifier requires a constant number of features (input variables), a character to be classified is transformed to a standard size, which is determined by WidthCharacterWidthCharacterWidthCharacterWidthCharacterwidthCharacterwidth_character and HeightCharacterHeightCharacterHeightCharacterHeightCharacterheightCharacterheight_character. The interpolation to be used for the transformation is determined by InterpolationInterpolationInterpolationInterpolationinterpolationinterpolation. It has the same meaning as in affine_trans_imageaffine_trans_imageAffineTransImageAffineTransImageAffineTransImageaffine_trans_image。The interpolation should be chosen such that no aliasing effects occur in the transformation. For most applications, InterpolationInterpolationInterpolationInterpolationinterpolationinterpolation = 'constant'"constant""constant""constant""constant""constant" should be used. It should be noted that the size of the transformed character is not chosen too large, because the generalization properties of the classifier may become bad for large sizes. In particular, for large sizes small segmentation errors will have a large influence on the computed features if gray value features are used. This happens because segmentation errors will change the smallest enclosing rectangle of the regions, thus the character is zoomed differently than the characters in the training set. In most applications, sizes between 6x8 and 10x14 should be used.

The parameter FeaturesFeaturesFeaturesFeaturesfeaturesfeatures can contain the following feature names for the classification of the characters.

'default'"default""default""default""default""default"

'ratio'"ratio""ratio""ratio""ratio""ratio" and 'pixel_invar'"pixel_invar""pixel_invar""pixel_invar""pixel_invar""pixel_invar" are selected.

'pixel'"pixel""pixel""pixel""pixel""pixel"

Gray values of the character (WidthCharacterWidthCharacterWidthCharacterWidthCharacterwidthCharacterwidth_character x HeightCharacterHeightCharacterHeightCharacterHeightCharacterheightCharacterheight_character features).

'pixel_invar'"pixel_invar""pixel_invar""pixel_invar""pixel_invar""pixel_invar"

Gray values of the character with maximum scaling of the gray values (WidthCharacterWidthCharacterWidthCharacterWidthCharacterwidthCharacterwidth_character x HeightCharacterHeightCharacterHeightCharacterHeightCharacterheightCharacterheight_character features).

'pixel_binary'"pixel_binary""pixel_binary""pixel_binary""pixel_binary""pixel_binary"

Region of the character as a binary image zoomed to a size of WidthCharacterWidthCharacterWidthCharacterWidthCharacterwidthCharacterwidth_character x HeightCharacterHeightCharacterHeightCharacterHeightCharacterheightCharacterheight_character (WidthCharacterWidthCharacterWidthCharacterWidthCharacterwidthCharacterwidth_character x HeightCharacterHeightCharacterHeightCharacterHeightCharacterheightCharacterheight_character features).

'gradient_8dir'"gradient_8dir""gradient_8dir""gradient_8dir""gradient_8dir""gradient_8dir"

Gradients are computed on the character image. The gradient directions are discretized into 8 directions. The amplitude image is decomposed into 8 channels according to these discretized directions. 25 samples on a 5x5 grid are extracted from each channel. These samples are used as features (200 features).

'projection_horizontal'"projection_horizontal""projection_horizontal""projection_horizontal""projection_horizontal""projection_horizontal"

Horizontal projection of the gray values (see gray_projectionsgray_projectionsGrayProjectionsGrayProjectionsGrayProjectionsgray_projections, HeightCharacterHeightCharacterHeightCharacterHeightCharacterheightCharacterheight_character features).

'projection_horizontal_invar'"projection_horizontal_invar""projection_horizontal_invar""projection_horizontal_invar""projection_horizontal_invar""projection_horizontal_invar"

Maximally scaled horizontal projection of the gray values (HeightCharacterHeightCharacterHeightCharacterHeightCharacterheightCharacterheight_character features).

'projection_vertical'"projection_vertical""projection_vertical""projection_vertical""projection_vertical""projection_vertical"

Vertical projection of the gray values (see gray_projectionsgray_projectionsGrayProjectionsGrayProjectionsGrayProjectionsgray_projections, WidthCharacterWidthCharacterWidthCharacterWidthCharacterwidthCharacterwidth_character features).

'projection_vertical_invar'"projection_vertical_invar""projection_vertical_invar""projection_vertical_invar""projection_vertical_invar""projection_vertical_invar"

Maximally scaled vertical projection of the gray values (WidthCharacterWidthCharacterWidthCharacterWidthCharacterwidthCharacterwidth_character features).

'ratio'"ratio""ratio""ratio""ratio""ratio"

Aspect ratio of the character (see height_width_ratioheight_width_ratioHeightWidthRatioHeightWidthRatioHeightWidthRatioheight_width_ratio, 1 feature).

'anisometry'"anisometry""anisometry""anisometry""anisometry""anisometry"

Anisometry of the character (see eccentricityeccentricityEccentricityEccentricityEccentricityeccentricity, 1 feature).

'width'"width""width""width""width""width"

Width of the character before scaling the character to the standard size (not scale-invariant, see height_width_ratioheight_width_ratioHeightWidthRatioHeightWidthRatioHeightWidthRatioheight_width_ratio, 1 feature).

'height'"height""height""height""height""height"

Height of the character before scaling the character to the standard size (not scale-invariant, see height_width_ratioheight_width_ratioHeightWidthRatioHeightWidthRatioHeightWidthRatioheight_width_ratio, 1 feature).

'zoom_factor'"zoom_factor""zoom_factor""zoom_factor""zoom_factor""zoom_factor"

Difference in size between the character and the values of WidthCharacterWidthCharacterWidthCharacterWidthCharacterwidthCharacterwidth_character and HeightCharacterHeightCharacterHeightCharacterHeightCharacterheightCharacterheight_character (not scale-invariant, 1 feature).

'foreground'"foreground""foreground""foreground""foreground""foreground"

Fraction of pixels in the foreground (1 feature).

'foreground_grid_9'"foreground_grid_9""foreground_grid_9""foreground_grid_9""foreground_grid_9""foreground_grid_9"

Fraction of pixels in the foreground in a 3x3 grid within the smallest enclosing rectangle of the character (9 features).

'foreground_grid_16'"foreground_grid_16""foreground_grid_16""foreground_grid_16""foreground_grid_16""foreground_grid_16"

Fraction of pixels in the foreground in a 4x4 grid within the smallest enclosing rectangle of the character (16 features).

'compactness'"compactness""compactness""compactness""compactness""compactness"

Compactness of the character (see compactnesscompactnessCompactnessCompactnessCompactnesscompactness, 1 feature).

'convexity'"convexity""convexity""convexity""convexity""convexity"

Convexity of the character (see convexityconvexityConvexityConvexityConvexityconvexity, 1 feature).

'moments_region_2nd_invar'"moments_region_2nd_invar""moments_region_2nd_invar""moments_region_2nd_invar""moments_region_2nd_invar""moments_region_2nd_invar"

Normalized 2nd moments of the character (see moments_region_2nd_invarmoments_region_2nd_invarMomentsRegion2ndInvarMomentsRegion2ndInvarMomentsRegion2ndInvarmoments_region_2nd_invar, 3 features).

'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"

Normalized 2nd relative moments of the character (see moments_region_2nd_rel_invarmoments_region_2nd_rel_invarMomentsRegion2ndRelInvarMomentsRegion2ndRelInvarMomentsRegion2ndRelInvarmoments_region_2nd_rel_invar, 2 features).

'moments_region_3rd_invar'"moments_region_3rd_invar""moments_region_3rd_invar""moments_region_3rd_invar""moments_region_3rd_invar""moments_region_3rd_invar"

Normalized 3rd moments of the character (see moments_region_3rd_invarmoments_region_3rd_invarMomentsRegion3rdInvarMomentsRegion3rdInvarMomentsRegion3rdInvarmoments_region_3rd_invar, 4 features).

'moments_central'"moments_central""moments_central""moments_central""moments_central""moments_central"

Normalized central moments of the character (see moments_region_centralmoments_region_centralMomentsRegionCentralMomentsRegionCentralMomentsRegionCentralmoments_region_central, 4 features).

'moments_gray_plane'"moments_gray_plane""moments_gray_plane""moments_gray_plane""moments_gray_plane""moments_gray_plane"

Normalized gray value moments and the angle of the gray value plane (see moments_gray_planemoments_gray_planeMomentsGrayPlaneMomentsGrayPlaneMomentsGrayPlanemoments_gray_plane, 4 features).

'phi'"phi""phi""phi""phi""phi"

Orientation (angle) of the character (see elliptic_axiselliptic_axisEllipticAxisEllipticAxisEllipticAxiselliptic_axis, 1 feature).

'num_connect'"num_connect""num_connect""num_connect""num_connect""num_connect"

Number of connected components (see connect_and_holesconnect_and_holesConnectAndHolesConnectAndHolesConnectAndHolesconnect_and_holes, 1 feature).

'num_holes'"num_holes""num_holes""num_holes""num_holes""num_holes"

Number of holes (see connect_and_holesconnect_and_holesConnectAndHolesConnectAndHolesConnectAndHolesconnect_and_holes, 1 feature).

'cooc'"cooc""cooc""cooc""cooc""cooc"

Values of the binary cooccurrence matrix (see gen_cooc_matrixgen_cooc_matrixGenCoocMatrixGenCoocMatrixGenCoocMatrixgen_cooc_matrix, 12 features).

'num_runs'"num_runs""num_runs""num_runs""num_runs""num_runs"

Number of runs in the region normalized by the height (1 feature).

'chord_histo'"chord_histo""chord_histo""chord_histo""chord_histo""chord_histo"

Frequency of the runs per row (not scale-invariant, HeightCharacterHeightCharacterHeightCharacterHeightCharacterheightCharacterheight_character features).

After the classifier has been created, it is trained using trainf_ocr_class_svmtrainf_ocr_class_svmTrainfOcrClassSvmTrainfOcrClassSvmTrainfOcrClassSvmtrainf_ocr_class_svm。After this, the classifier can be saved using write_ocr_class_svmwrite_ocr_class_svmWriteOcrClassSvmWriteOcrClassSvmWriteOcrClassSvmwrite_ocr_class_svm。Alternatively, the classifier can be used immediately after training to classify characters using do_ocr_single_class_svmdo_ocr_single_class_svmDoOcrSingleClassSvmDoOcrSingleClassSvmDoOcrSingleClassSvmdo_ocr_single_class_svm or do_ocr_multi_class_svmdo_ocr_multi_class_svmDoOcrMultiClassSvmDoOcrMultiClassSvmDoOcrMultiClassSvmdo_ocr_multi_class_svm。

A comparison of SVM and the multi-layer perceptron (MLP) (see create_ocr_class_mlpcreate_ocr_class_mlpCreateOcrClassMlpCreateOcrClassMlpCreateOcrClassMlpcreate_ocr_class_mlp) typically shows that SVMs are generally faster at training, especially for huge training sets, and achieve slightly better recognition rates than MLPs. The MLP is faster at classification and should therefore be preferred in time critical applications. Please note that this guideline assumes optimal tuning of the parameters.

执行信息

• 多线程类型：可重入（与非独占算子并行运行）。
• 多线程作用域：全局（可从任何线程调用）。
• 未采用并行化处理。

此算子返回一个句柄。请注意，即使该句柄被用作特定算子的输入参数，这些算子仍可能改变此句柄类型的实例状态。

参数

示例（HDevelop）

read_image (Image, 'letters')
* Segment the image.
binary_threshold(Image,&Region, 'otsu', 'dark', &UsedThreshold);
dilation_circle (Region, RegionDilation, 3.5)
connection (RegionDilation, ConnectedRegions)
intersection (ConnectedRegions, Region, RegionIntersection)
sort_region (RegionIntersection, Characters, 'character', 'true', 'row')
* Generate the training file.
count_obj (Characters, Number)
Classes := []
for J := 0 to 25 by 1
    Classes := [Classes,gen_tuple_const(20,chr(ord('a')+J))]
endfor
Classes := [Classes,gen_tuple_const(20,'.')]
write_ocr_trainf (Characters, Image, Classes, 'letters.trf')
* Generate and train the classifier.
read_ocr_trainf_names ('letters.trf', CharacterNames, CharacterCount)
create_ocr_class_svm (8, 10, 'constant', 'default', CharacterNames, \
                      'rbf', 0.01, 0.01, 'one-versus-all', \
                      'principal_components', 10, OCRHandle)
trainf_ocr_class_svm (OCRHandle, 'letters.trf', 0.001, 'default')
* Re-classify the characters in the image.
do_ocr_multi_class_svm (Characters, Image, OCRHandle, Class)

结果

如果参数有效，算子 create_ocr_class_svmcreate_ocr_class_svmCreateOcrClassSvmCreateOcrClassSvmCreateOcrClassSvmcreate_ocr_class_svm 返回值 2 ( H_MSG_TRUE )。如有必要，则抛出异常。

可能的后继

trainf_ocr_class_svmtrainf_ocr_class_svmTrainfOcrClassSvmTrainfOcrClassSvmTrainfOcrClassSvmtrainf_ocr_class_svm

替代

create_ocr_class_mlpcreate_ocr_class_mlpCreateOcrClassMlpCreateOcrClassMlpCreateOcrClassMlpcreate_ocr_class_mlp

另见

do_ocr_single_class_svmdo_ocr_single_class_svmDoOcrSingleClassSvmDoOcrSingleClassSvmDoOcrSingleClassSvmdo_ocr_single_class_svm, do_ocr_multi_class_svmdo_ocr_multi_class_svmDoOcrMultiClassSvmDoOcrMultiClassSvmDoOcrMultiClassSvmdo_ocr_multi_class_svm, clear_ocr_class_svmclear_ocr_class_svmClearOcrClassSvmClearOcrClassSvmClearOcrClassSvmclear_ocr_class_svm, create_class_svmcreate_class_svmCreateClassSvmCreateClassSvmCreateClassSvmcreate_class_svm, train_class_svmtrain_class_svmTrainClassSvmTrainClassSvmTrainClassSvmtrain_class_svm, classify_class_svmclassify_class_svmClassifyClassSvmClassifyClassSvmClassifyClassSvmclassify_class_svm

模块

光学字符识别/光学字符验证

---