char_thresholdchar_thresholdCharThresholdCharThresholdchar_threshold (Operator)
Name
char_thresholdchar_thresholdCharThresholdCharThresholdchar_threshold — Perform a threshold segmentation for extracting characters.
Signature
Herror char_threshold(const Hobject Image, const Hobject HistoRegion, Hobject* Characters, double Sigma, double Percent, Hlong* Threshold)
Herror T_char_threshold(const Hobject Image, const Hobject HistoRegion, Hobject* Characters, const Htuple Sigma, const Htuple Percent, Htuple* Threshold)
void CharThreshold(const HObject& Image, const HObject& HistoRegion, HObject* Characters, const HTuple& Sigma, const HTuple& Percent, HTuple* Threshold)
HRegion HImage::CharThreshold(const HRegion& HistoRegion, double Sigma, const HTuple& Percent, HTuple* Threshold) const
HRegion HImage::CharThreshold(const HRegion& HistoRegion, double Sigma, double Percent, Hlong* Threshold) const
static void HOperatorSet.CharThreshold(HObject image, HObject histoRegion, out HObject characters, HTuple sigma, HTuple percent, out HTuple threshold)
HRegion HImage.CharThreshold(HRegion histoRegion, double sigma, HTuple percent, out HTuple threshold)
HRegion HImage.CharThreshold(HRegion histoRegion, double sigma, double percent, out int threshold)
def char_threshold(image: HObject, histo_region: HObject, sigma: float, percent: Union[int, float]) -> Tuple[HObject, Sequence[int]]
def char_threshold_s(image: HObject, histo_region: HObject, sigma: float, percent: Union[int, float]) -> Tuple[HObject, int]
Description
The main application of char_thresholdchar_thresholdCharThresholdCharThresholdCharThresholdchar_threshold is to segment single-channel
images of dark characters on bright paper. The operator works as follows:
First, a histogram of the gray values in the image ImageImageImageImageimageimage is computed
for the points in the region HistoRegionHistoRegionHistoRegionHistoRegionhistoRegionhisto_region{}. To eliminate noise, the
histogram is smoothed with the given SigmaSigmaSigmaSigmasigmasigma (Gaussian smoothing). In
the histogram, the background (white paper) corresponds to a large peak at
high gray values, while the characters form a small peak at low gray
values. In contrast to the operator binary_thresholdbinary_thresholdBinaryThresholdBinaryThresholdBinaryThresholdbinary_threshold
(with MethodMethodMethodMethodmethodmethod='smooth_histo'"smooth_histo""smooth_histo""smooth_histo""smooth_histo""smooth_histo"), which locates the
minimum between the two peaks, here the threshold for the segmentation is
determined in relation to the maximum of the histogram, i.e., the background,
with the following condition:
histogram[threshold] * 100.0 < histogram[maximum] * (100.0 - PercentPercentPercentPercentpercentpercent)
For example, if you choose PercentPercentPercentPercentpercentpercent = 95 the operator locates the
gray value whose frequency is at most 5 percent of the maximum
frequency. Because char_thresholdchar_thresholdCharThresholdCharThresholdCharThresholdchar_threshold assumes that the characters are
darker than the background, the threshold is searched for “to the left” of
the maximum.
In comparison to binary_thresholdbinary_thresholdBinaryThresholdBinaryThresholdBinaryThresholdbinary_threshold, this operator should be used if
there is no clear minimum between the histogram peaks corresponding to the
characters and the background, respectively, or if there is no peak
corresponding to the characters at all. This may happen, e.g., if the image
contains only few characters or in the case of a non-uniform illumination.
Execution Information
- Multithreading type: reentrant (runs in parallel with non-exclusive operators).
- Multithreading scope: global (may be called from any thread).
- Automatically parallelized on tuple level.
- Automatically parallelized on internal data level.
Parameters
ImageImageImageImageimageimage (input_object) singlechannelimage(-array) → objectHImageHObjectHImageHobject (byte)
Input image.
HistoRegionHistoRegionHistoRegionHistoRegionhistoRegionhisto_region (input_object) region → objectHRegionHObjectHRegionHobject
Region in which the histogram is computed.
CharactersCharactersCharactersCharacterscharacterscharacters (output_object) region(-array) → objectHRegionHObjectHRegionHobject *
Dark regions (characters).
SigmaSigmaSigmaSigmasigmasigma (input_control) number → HTuplefloatHTupleHtuple (real) (double) (double) (double)
Sigma for the Gaussian smoothing of the histogram.
Default value: 2.0
Suggested values: 0.0, 0.5, 1.0, 2.0, 3.0, 4.0, 5.0
Typical range of values: 0.0
≤
Sigma
Sigma
Sigma
Sigma
sigma
sigma
≤
50.0 (lin)
Minimum increment: 0.01
Recommended increment: 0.2
PercentPercentPercentPercentpercentpercent (input_control) number → HTupleUnion[int, float]HTupleHtuple (real / integer) (double / int / long) (double / Hlong) (double / Hlong)
Percentage for the gray value difference.
Default value: 95
Suggested values: 90, 92, 95, 96, 97, 98, 99, 99.5, 100
Typical range of values: 0.0
≤
Percent
Percent
Percent
Percent
percent
percent
≤
100.0 (lin)
Minimum increment: 0.1
Recommended increment: 0.5
ThresholdThresholdThresholdThresholdthresholdthreshold (output_control) integer(-array) → HTupleSequence[int]HTupleHtuple (integer) (int / long) (Hlong) (Hlong)
Calculated threshold.
Example (HDevelop)
read_image (Image, 'letters')
char_threshold (Image, Image, Seg, 0.0, 5.0, Threshold)
connection (Seg, Connected)
Example (C)
read_image(&Image,"letters");
char_threshold(Image,Image,&Seg,0.0,5.0,&Threshold);
connection(Seg,&Connected);
set_colored(WindowHandle,12);
set_shape(WindowHandle,"rectangle1");
disp_region(Connected,WindowHandle);
Example (HDevelop)
read_image (Image, 'letters')
char_threshold (Image, Image, Seg, 0.0, 5.0, Threshold)
connection (Seg, Connected)
Example (HDevelop)
read_image (Image, 'letters')
char_threshold (Image, Image, Seg, 0.0, 5.0, Threshold)
connection (Seg, Connected)
Example (HDevelop)
read_image (Image, 'letters')
char_threshold (Image, Image, Seg, 0.0, 5.0, Threshold)
connection (Seg, Connected)
Possible Predecessors
anisotropic_diffusionanisotropic_diffusionAnisotropicDiffusionAnisotropicDiffusionAnisotropicDiffusionanisotropic_diffusion,
median_imagemedian_imageMedianImageMedianImageMedianImagemedian_image,
illuminateilluminateIlluminateIlluminateIlluminateilluminate
Possible Successors
connectionconnectionConnectionConnectionConnectionconnection,
select_shapeselect_shapeSelectShapeSelectShapeSelectShapeselect_shape,
select_grayselect_graySelectGraySelectGraySelectGrayselect_gray
Alternatives
binary_thresholdbinary_thresholdBinaryThresholdBinaryThresholdBinaryThresholdbinary_threshold,
auto_thresholdauto_thresholdAutoThresholdAutoThresholdAutoThresholdauto_threshold,
gray_histogray_histoGrayHistoGrayHistoGrayHistogray_histo,
smooth_funct_1d_gausssmooth_funct_1d_gaussSmoothFunct1dGaussSmoothFunct1dGaussSmoothFunct1dGausssmooth_funct_1d_gauss,
thresholdthresholdThresholdThresholdThresholdthreshold
Module
Foundation