// Adds sub-pixel resolution EdgeOffsets for the outline using only
// a binary image source.
// Runs a sliding window of 5 edge steps over the outline, maintaining a count
// of the number of steps in each of the 4 directions in the window, and a
// sum of the x or y position of each step (as appropriate to its direction.)
// Ignores single-count steps EXCEPT the sharp U-turn and smoothes out the
// perpendicular direction. Eg
// ___ ___ Chain code from the left:
// |___ ___ ___| 222122212223221232223000
// |___| |_| Corresponding counts of each direction:
// 0 00000000000000000123
// 1 11121111001111100000
// 2 44434443443333343321
// 3 00000001111111112111
// Count of direction at center 41434143413313143313
// Step gets used? YNYYYNYYYNYYNYNYYYyY (y= U-turn exception)
// Path redrawn showing only the used points:
// ___ ___
// ___ ___ ___|
// ___ _
// Sub-pixel edge position cannot be shown well with ASCII-art, but each
// horizontal step's y position is the mean of the y positions of the steps
// in the same direction in the sliding window, which makes a much smoother
// outline, without losing important detail.
void C_OUTLINE::ComputeBinaryOffsets() {
delete [] offsets;
offsets = new EdgeOffset[stepcount];
// Count of the number of steps in each direction in the sliding window.
int dir_counts[4];
// Sum of the positions (y for a horizontal step, x for vertical) in each
// direction in the sliding window.
int pos_totals[4];
memset(dir_counts, 0, sizeof(dir_counts));
memset(pos_totals, 0, sizeof(pos_totals));
ICOORD pos = start;
ICOORD tail_pos = pos;
// tail_pos is the trailing position, with the next point to be lost from
// the window.
tail_pos -= step(stepcount - 1);
tail_pos -= step(stepcount - 2);
// head_pos is the leading position, with the next point to be added to the
// window.
ICOORD head_pos = tail_pos;
// Set up the initial window with 4 points in [-2, 2)
for (int s = -2; s < 2; ++s) {
increment_step(s, 1, &head_pos, dir_counts, pos_totals);
}
for (int s = 0; s < stepcount; pos += step(s++)) {
// At step s, s in in the middle of [s-2, s+2].
increment_step(s + 2, 1, &head_pos, dir_counts, pos_totals);
int dir_index = chain_code(s);
ICOORD step_vec = step(s);
int best_diff = 0;
int offset = 0;
// Use only steps that have a count of >=2 OR the strong U-turn with a
// single d and 2 at d-1 and 2 at d+1 (mod 4).
if (dir_counts[dir_index] >= 2 || (dir_counts[dir_index] == 1 &&
dir_counts[Modulo(dir_index - 1, 4)] == 2 &&
dir_counts[Modulo(dir_index + 1, 4)] == 2)) {
// Valid step direction.
best_diff = dir_counts[dir_index];
int edge_pos = step_vec.x() == 0 ? pos.x() : pos.y();
// The offset proposes that the actual step should be positioned at
// the mean position of the steps in the window of the same direction.
// See ASCII art above.
offset = pos_totals[dir_index] - best_diff * edge_pos;
}
offsets[s].offset_numerator =
static_cast<inT8>(ClipToRange(offset, -MAX_INT8, MAX_INT8));
offsets[s].pixel_diff = static_cast<uinT8>(ClipToRange(best_diff, 0 ,
MAX_UINT8));
// The direction is just the vector from start to end of the window.
FCOORD direction(head_pos.x() - tail_pos.x(), head_pos.y() - tail_pos.y());
offsets[s].direction = direction.to_direction();
increment_step(s - 2, -1, &tail_pos, dir_counts, pos_totals);
}
}
/**
* Returns the bounding rectangle of the current object at the given level in
* the coordinates of the working image that is pix_binary().
* See comment on coordinate system above.
* Returns false if there is no such object at the current position.
*/
bool PageIterator::BoundingBoxInternal(PageIteratorLevel level,
int* left, int* top,
int* right, int* bottom) const {
if (Empty(level))
return false;
TBOX box;
PARA *para = NULL;
switch (level) {
case RIL_BLOCK:
box = it_->block()->block->bounding_box();
break;
case RIL_PARA:
para = it_->row()->row->para();
// explicit fall-through.
case RIL_TEXTLINE:
box = it_->row()->row->bounding_box();
break;
case RIL_WORD:
box = it_->word()->word->bounding_box();
break;
case RIL_SYMBOL:
if (cblob_it_ == NULL)
box = it_->word()->box_word->BlobBox(blob_index_);
else
box = cblob_it_->data()->bounding_box();
}
if (level == RIL_PARA) {
PageIterator other = *this;
other.Begin();
do {
if (other.it_->block() &&
other.it_->block()->block == it_->block()->block &&
other.it_->row() && other.it_->row()->row &&
other.it_->row()->row->para() == para) {
box = box.bounding_union(other.it_->row()->row->bounding_box());
}
} while (other.Next(RIL_TEXTLINE));
}
if (level != RIL_SYMBOL || cblob_it_ != NULL)
box.rotate(it_->block()->block->re_rotation());
// Now we have a box in tesseract coordinates relative to the image rectangle,
// we have to convert the coords to a top-down system.
const int pix_height = pixGetHeight(tesseract_->pix_binary());
const int pix_width = pixGetWidth(tesseract_->pix_binary());
*left = ClipToRange(static_cast<int>(box.left()), 0, pix_width);
*top = ClipToRange(pix_height - box.top(), 0, pix_height);
*right = ClipToRange(static_cast<int>(box.right()), *left, pix_width);
*bottom = ClipToRange(pix_height - box.bottom(), *top, pix_height);
return true;
}
// Returns an INT_FEATURE_STRUCT corresponding to the given bucket coords.
INT_FEATURE_STRUCT IntFeatureSpace::PositionFromBuckets(int x,
int y,
int theta) const {
INT_FEATURE_STRUCT pos = {
static_cast<uinT8>(ClipToRange(
(x * kIntFeatureExtent + kIntFeatureExtent / 2) / x_buckets_,
0, MAX_UINT8)),
static_cast<uinT8>(ClipToRange(
(y * kIntFeatureExtent + kIntFeatureExtent / 2) / y_buckets_,
0, MAX_UINT8)),
static_cast<uinT8>(ClipToRange(
DivRounded(theta * kIntFeatureExtent, theta_buckets_),
0, MAX_UINT8))};
return pos;
}
// Helper for SetupNonLinear computes an image of shortest run-lengths from
// the x/y edges provided.
// Based on "A nonlinear normalization method for handprinted Kanji character
// recognition -- line density equalization" by Hiromitsu Yamada et al.
// Eg below is an O in a 1-pixel margin-ed bounding box and the corresponding
// ______________ input x_coords and y_coords.
// | _________ | <empty>
// | | _ | | 1, 6
// | | | | | | 1, 3, 4, 6
// | | | | | | 1, 3, 4, 6
// | | | | | | 1, 3, 4, 6
// | | |_| | | 1, 3, 4, 6
// | |_________| | 1, 6
// |_____________| <empty>
// E 1 1 1 1 1 E
// m 7 7 2 7 7 m
// p 6 p
// t 7 t
// y y
// The output image contains the min of the x and y run-length (distance
// between edges) at each coordinate in the image thus:
// ______________
// |7 1_1_1_1_1 7|
// |1|5 5 1 5 5|1|
// |1|2 2|1|2 2|1|
// |1|2 2|1|2 2|1|
// |1|2 2|1|2 2|1|
// |1|2 2|1|2 2|1|
// |1|5_5_1_5_5|1|
// |7_1_1_1_1_1_7|
// Note that the input coords are all integer, so all partial pixels are dealt
// with elsewhere. Although it is nice for outlines to be properly connected
// and continuous, there is no requirement that they be as such, so they could
// have been derived from a flaky source, such as greyscale.
// This function works only within the provided box, and it is assumed that the
// input x_coords and y_coords have already been translated to have the bottom-
// left of box as the origin. Although an output, the minruns should have been
// pre-initialized to be the same size as box. Each element will contain the
// minimum of x and y run-length as shown above.
static void ComputeRunlengthImage(
const TBOX& box,
const GenericVector<GenericVector<int> >& x_coords,
const GenericVector<GenericVector<int> >& y_coords,
GENERIC_2D_ARRAY<int>* minruns) {
int width = box.width();
int height = box.height();
ASSERT_HOST(minruns->dim1() == width);
ASSERT_HOST(minruns->dim2() == height);
// Set a 2-d image array to the run lengths at each pixel.
for (int ix = 0; ix < width; ++ix) {
int y = 0;
for (int i = 0; i < y_coords[ix].size(); ++i) {
int y_edge = ClipToRange(y_coords[ix][i], 0, height);
int gap = y_edge - y;
// Every pixel between the last and current edge get set to the gap.
while (y < y_edge) {
(*minruns)(ix, y) = gap;
++y;
}
}
// Pretend there is a bounding box of edges all around the image.
int gap = height - y;
while (y < height) {
(*minruns)(ix, y) = gap;
++y;
}
}
// Now set the image pixels the the MIN of the x and y runlengths.
for (int iy = 0; iy < height; ++iy) {
int x = 0;
for (int i = 0; i < x_coords[iy].size(); ++i) {
int x_edge = ClipToRange(x_coords[iy][i], 0, width);
int gap = x_edge - x;
while (x < x_edge) {
if (gap < (*minruns)(x, iy))
(*minruns)(x, iy) = gap;
++x;
}
}
int gap = width - x;
while (x < width) {
if (gap < (*minruns)(x, iy))
(*minruns)(x, iy) = gap;
++x;
}
}
}
void DENORM::LocalNormTransform(const FCOORD& pt, FCOORD* transformed) const {
FCOORD translated(pt.x() - x_origin_, pt.y() - y_origin_);
if (x_map_ != NULL && y_map_ != NULL) {
int x = ClipToRange(IntCastRounded(translated.x()), 0, x_map_->size()-1);
translated.set_x((*x_map_)[x]);
int y = ClipToRange(IntCastRounded(translated.y()), 0, y_map_->size()-1);
translated.set_y((*y_map_)[y]);
} else {
translated.set_x(translated.x() * x_scale_);
translated.set_y(translated.y() * y_scale_);
if (rotation_ != NULL)
translated.rotate(*rotation_);
}
transformed->set_x(translated.x() + final_xshift_);
transformed->set_y(translated.y() + final_yshift_);
}
/**
* Returns the bounding rectangle of the current object at the given level in
* coordinates of the original image.
* See comment on coordinate system above.
* Returns false if there is no such object at the current position.
*/
bool PageIterator::BoundingBox(PageIteratorLevel level,
int* left, int* top,
int* right, int* bottom) const {
if (!BoundingBoxInternal(level, left, top, right, bottom))
return false;
// Convert to the coordinate system of the original image.
*left = ClipToRange(*left / scale_ + rect_left_,
rect_left_, rect_left_ + rect_width_);
*top = ClipToRange(*top / scale_ + rect_top_,
rect_top_, rect_top_ + rect_height_);
*right = ClipToRange((*right + scale_ - 1) / scale_ + rect_left_,
*left, rect_left_ + rect_width_);
*bottom = ClipToRange((*bottom + scale_ - 1) / scale_ + rect_top_,
*top, rect_top_ + rect_height_);
return true;
}
static void HistogramWeight(double weight, STATS* histogram) {
int bucket = kHistogramBuckets - 1;
if (weight != 0.0) {
double logval = -log2(fabs(weight));
bucket = ClipToRange(IntCastRounded(logval), 0, kHistogramBuckets - 1);
}
histogram->add(bucket, 1);
}
// Applies params model weights to the given features.
// Assumes that features is an array of size PTRAIN_NUM_FEATURE_TYPES.
// The cost is set to a number that can be multiplied by the outline length,
// as with the old ratings scheme. This enables words of different length
// and combinations of words to be compared meaningfully.
float ParamsModel::ComputeCost(const float features[]) const {
float unnorm_score = 0.0;
for (int f = 0; f < PTRAIN_NUM_FEATURE_TYPES; ++f) {
unnorm_score += weights_vec_[pass_][f] * features[f];
}
return ClipToRange(-unnorm_score / kScoreScaleFactor,
kMinFinalCost, kMaxFinalCost);
}
// Constructs and returns a copy randomized by the method given by
// the randomizer index. If index is out of [0, kSampleRandomSize) then
// an exact copy is returned.
TrainingSample* TrainingSample::RandomizedCopy(int index) const {
TrainingSample* sample = Copy();
if (index >= 0 && index < kSampleRandomSize) {
++index; // Remove the first combination.
int yshift = kYShiftValues[index / kSampleScaleSize];
double scaling = kScaleValues[index % kSampleScaleSize];
for (int i = 0; i < num_features_; ++i) {
double result = (features_[i].X - kRandomizingCenter) * scaling;
result += kRandomizingCenter;
sample->features_[i].X = ClipToRange(static_cast<int>(result + 0.5), 0,
MAX_UINT8);
result = (features_[i].Y - kRandomizingCenter) * scaling;
result += kRandomizingCenter + yshift;
sample->features_[i].Y = ClipToRange(static_cast<int>(result + 0.5), 0,
MAX_UINT8);
}
}
return sample;
}
// Fills in the x-height range accepted by the given unichar_id, given its
// bounding box in the usual baseline-normalized coordinates, with some
// initial crude x-height estimate (such as word size) and this denoting the
// transformation that was used. Returns false, and an empty range if the
// bottom is a mis-fit. Returns true and empty [0, 0] range if the bottom
// fits, but the top is impossible.
bool DENORM::XHeightRange(int unichar_id, const UNICHARSET& unicharset,
const TBOX& bbox,
inT16* min_xht, inT16* max_xht) const {
// Clip the top and bottom to the limit of normalized feature space.
int top = ClipToRange<int>(bbox.top(), 0, kBlnCellHeight - 1);
int bottom = ClipToRange<int>(bbox.bottom(), 0, kBlnCellHeight - 1);
// A tolerance of yscale corresponds to 1 pixel in the image.
double tolerance = y_scale();
int min_bottom, max_bottom, min_top, max_top;
unicharset.get_top_bottom(unichar_id, &min_bottom, &max_bottom,
&min_top, &max_top);
// Default returns indicate a mis-fit.
*min_xht = 0;
*max_xht = 0;
// Chars with a misfitting bottom might be sub/superscript/dropcap, or might
// just be wrongly classified. Return an empty range so they have to be
// good to be considered.
if (bottom < min_bottom - tolerance || bottom > max_bottom + tolerance) {
return false;
}
// To help very high cap/xheight ratio fonts accept the correct x-height,
// and to allow the large caps in small caps to accept the xheight of the
// small caps, add kBlnBaselineOffset to chars with a maximum max.
if (max_top == kBlnCellHeight - 1)
max_top += kBlnBaselineOffset;
int height = top - kBlnBaselineOffset;
double min_height = min_top - kBlnBaselineOffset - tolerance;
double max_height = max_top - kBlnBaselineOffset + tolerance;
if (min_height <= 0.0) {
if (height <= 0 || max_height > 0)
*max_xht = MAX_INT16; // Anything will do.
} else if (height > 0) {
int result = IntCastRounded(height * kBlnXHeight / y_scale() / min_height);
*max_xht = static_cast<inT16>(ClipToRange(result, 0, MAX_INT16));
}
if (max_height > 0.0 && height > 0) {
int result = IntCastRounded(height * kBlnXHeight / y_scale() / max_height);
*min_xht = static_cast<inT16>(ClipToRange(result, 0, MAX_INT16));
}
return true;
}
/**
* For each class in unicharset, computes the match between
* norm_feature and the normalization protos for that class.
* Converts this number to the range from 0 - 255 and stores it
* into char_norm_array. CharNormArray is indexed by unichar_id.
*
* Globals:
* - PreTrainedTemplates current set of built-in templates
*
* @param norm_feature character normalization feature
* @param[out] char_norm_array place to put results of size unicharset.size()
*
* @note Exceptions: none
* @note History: Wed Feb 20 11:20:54 1991, DSJ, Created.
*/
void Classify::ComputeIntCharNormArray(const FEATURE_STRUCT& norm_feature,
uinT8* char_norm_array) {
for (int i = 0; i < unicharset.size(); i++) {
if (i < PreTrainedTemplates->NumClasses) {
int norm_adjust = static_cast<int>(INT_CHAR_NORM_RANGE *
ComputeNormMatch(i, norm_feature, FALSE));
char_norm_array[i] = ClipToRange(norm_adjust, 0, MAX_INT_CHAR_NORM);
} else {
// Classes with no templates (eg. ambigs & ligatures) default
// to worst match.
char_norm_array[i] = MAX_INT_CHAR_NORM;
}
}
} /* ComputeIntCharNormArray */
// Normalize in-place and record the normalization in the DENORM.
void TWERD::Normalize(ROW* row, float x_height, bool numeric_mode,
DENORM* denorm) {
TBOX word_box = bounding_box();
DENORM antidote((word_box.left() + word_box.right()) / 2.0,
kBlnXHeight / x_height, row);
if (row == NULL) {
antidote = DENORM(antidote.origin(), antidote.scale(), 0.0,
word_box.bottom(), 0, NULL, false, NULL);
}
int num_segments = 0;
DENORM_SEG *segs = new DENORM_SEG[NumBlobs()];
for (TBLOB* blob = blobs; blob != NULL; blob = blob->next) {
TBOX blob_box = blob->bounding_box();
ICOORD translation(-static_cast<int>(floor(antidote.origin() + 0.5)),
-blob_box.bottom());
float factor = antidote.scale();
if (numeric_mode) {
factor = ClipToRange(kBlnXHeight * 4.0f / (3 * blob_box.height()),
factor, factor * 1.5f);
segs[num_segments].xstart = blob->bounding_box().left();
segs[num_segments].ycoord = blob_box.bottom();
segs[num_segments++].scale_factor = factor;
} else {
float blob_x_center = (blob_box.left() + blob_box.right()) / 2.0;
float y_shift = antidote.yshift_at_orig_x(blob_x_center);
translation.set_y(-static_cast<int>(floor(y_shift + 0.5)));
}
blob->Move(translation);
blob->Scale(factor);
blob->Move(ICOORD(0, kBlnBaselineOffset));
}
if (num_segments > 0) {
antidote.set_segments(segs, num_segments);
}
delete [] segs;
if (denorm != NULL)
*denorm = antidote;
}
WordFeature::WordFeature(const FCOORD& fcoord, uinT8 dir)
: x_(IntCastRounded(fcoord.x())),
y_(ClipToRange(IntCastRounded(fcoord.y()), 0, MAX_UINT8)),
dir_(dir) {
}
// Adds sub-pixel resolution EdgeOffsets for the outline if the supplied
// pix is 8-bit. Does nothing otherwise.
// Operation: Consider the following near-horizontal line:
// _________
// |________
// |________
// At *every* position along this line, the gradient direction will be close
// to vertical. Extrapoaltion/interpolation of the position of the threshold
// that was used to binarize the image gives a more precise vertical position
// for each horizontal step, and the conflict in step direction and gradient
// direction can be used to ignore the vertical steps.
void C_OUTLINE::ComputeEdgeOffsets(int threshold, Pix* pix) {
if (pixGetDepth(pix) != 8) return;
const l_uint32* data = pixGetData(pix);
int wpl = pixGetWpl(pix);
int width = pixGetWidth(pix);
int height = pixGetHeight(pix);
bool negative = flag(COUT_INVERSE);
delete [] offsets;
offsets = new EdgeOffset[stepcount];
ICOORD pos = start;
ICOORD prev_gradient;
ComputeGradient(data, wpl, pos.x(), height - pos.y(), width, height,
&prev_gradient);
for (int s = 0; s < stepcount; ++s) {
ICOORD step_vec = step(s);
TPOINT pt1(pos);
pos += step_vec;
TPOINT pt2(pos);
ICOORD next_gradient;
ComputeGradient(data, wpl, pos.x(), height - pos.y(), width, height,
&next_gradient);
// Use the sum of the prev and next as the working gradient.
ICOORD gradient = prev_gradient + next_gradient;
// best_diff will be manipulated to be always positive.
int best_diff = 0;
// offset will be the extrapolation of the location of the greyscale
// threshold from the edge with the largest difference, relative to the
// location of the binary edge.
int offset = 0;
if (pt1.y == pt2.y && abs(gradient.y()) * 2 >= abs(gradient.x())) {
// Horizontal step. diff_sign == 1 indicates black above.
int diff_sign = (pt1.x > pt2.x) == negative ? 1 : -1;
int x = MIN(pt1.x, pt2.x);
int y = height - pt1.y;
int best_sum = 0;
int best_y = y;
EvaluateVerticalDiff(data, wpl, diff_sign, x, y, height,
&best_diff, &best_sum, &best_y);
// Find the strongest edge.
int test_y = y;
do {
++test_y;
} while (EvaluateVerticalDiff(data, wpl, diff_sign, x, test_y, height,
&best_diff, &best_sum, &best_y));
test_y = y;
do {
--test_y;
} while (EvaluateVerticalDiff(data, wpl, diff_sign, x, test_y, height,
&best_diff, &best_sum, &best_y));
offset = diff_sign * (best_sum / 2 - threshold) +
(y - best_y) * best_diff;
} else if (pt1.x == pt2.x && abs(gradient.x()) * 2 >= abs(gradient.y())) {
// Vertical step. diff_sign == 1 indicates black on the left.
int diff_sign = (pt1.y > pt2.y) == negative ? 1 : -1;
int x = pt1.x;
int y = height - MAX(pt1.y, pt2.y);
const l_uint32* line = pixGetData(pix) + y * wpl;
int best_sum = 0;
int best_x = x;
EvaluateHorizontalDiff(line, diff_sign, x, width,
&best_diff, &best_sum, &best_x);
// Find the strongest edge.
int test_x = x;
do {
++test_x;
} while (EvaluateHorizontalDiff(line, diff_sign, test_x, width,
&best_diff, &best_sum, &best_x));
test_x = x;
do {
--test_x;
} while (EvaluateHorizontalDiff(line, diff_sign, test_x, width,
&best_diff, &best_sum, &best_x));
offset = diff_sign * (threshold - best_sum / 2) +
(best_x - x) * best_diff;
}
offsets[s].offset_numerator =
static_cast<inT8>(ClipToRange(offset, -MAX_INT8, MAX_INT8));
offsets[s].pixel_diff = static_cast<uinT8>(ClipToRange(best_diff, 0 ,
MAX_UINT8));
if (negative) gradient = -gradient;
// Compute gradient angle quantized to 256 directions, rotated by 64 (pi/2)
// to convert from gradient direction to edge direction.
offsets[s].direction =
Modulo(FCOORD::binary_angle_plus_pi(gradient.angle()) + 64, 256);
prev_gradient = next_gradient;
}
}
int TextlineProjection::ImageYToProjectionY(int y) const {
y = ClipToRange((y_origin_ - y) / scale_factor_, 0, pixGetHeight(pix_) - 1);
return y;
}
// Clip the given grid coordinates to fit within the grid.
void GridBase::ClipGridCoords(int* x, int* y) const {
*x = ClipToRange(*x, 0, gridwidth_ - 1);
*y = ClipToRange(*y, 0, gridheight_ - 1);
}
// Gets the properties for a grapheme string, combining properties for
// multiple characters in a meaningful way where possible.
// Returns false if no valid match was found in the unicharset.
// NOTE that script_id, mirror, and other_case refer to this unicharset on
// return and will need translation if the target unicharset is different.
bool UNICHARSET::GetStrProperties(const char* utf8_str,
UNICHAR_PROPERTIES* props) const {
props->Init();
props->SetRangesEmpty();
props->min_advance = 0;
props->max_advance = 0;
int total_unicodes = 0;
GenericVector<UNICHAR_ID> encoding;
if (!encode_string(utf8_str, true, &encoding, NULL, NULL))
return false; // Some part was invalid.
for (int i = 0; i < encoding.size(); ++i) {
int id = encoding[i];
const UNICHAR_PROPERTIES& src_props = unichars[id].properties;
// Logical OR all the bools.
if (src_props.isalpha) props->isalpha = true;
if (src_props.islower) props->islower = true;
if (src_props.isupper) props->isupper = true;
if (src_props.isdigit) props->isdigit = true;
if (src_props.ispunctuation) props->ispunctuation = true;
if (src_props.isngram) props->isngram = true;
if (src_props.enabled) props->enabled = true;
// Min/max the tops/bottoms.
UpdateRange(src_props.min_bottom, &props->min_bottom, &props->max_bottom);
UpdateRange(src_props.max_bottom, &props->min_bottom, &props->max_bottom);
UpdateRange(src_props.min_top, &props->min_top, &props->max_top);
UpdateRange(src_props.max_top, &props->min_top, &props->max_top);
int bearing = ClipToRange(props->min_advance + src_props.min_bearing,
-MAX_INT16, MAX_INT16);
if (total_unicodes == 0 || bearing < props->min_bearing)
props->min_bearing = bearing;
bearing = ClipToRange(props->max_advance + src_props.max_bearing,
-MAX_INT16, MAX_INT16);
if (total_unicodes == 0 || bearing < props->max_bearing)
props->max_bearing = bearing;
props->min_advance = ClipToRange(props->min_advance + src_props.min_advance,
-MAX_INT16, MAX_INT16);
props->max_advance = ClipToRange(props->max_advance + src_props.max_advance,
-MAX_INT16, MAX_INT16);
// With a single width, just use the widths stored in the unicharset.
props->min_width = src_props.min_width;
props->max_width = src_props.max_width;
// Use the first script id, other_case, mirror, direction.
// Note that these will need translation, except direction.
if (total_unicodes == 0) {
props->script_id = src_props.script_id;
props->other_case = src_props.other_case;
props->mirror = src_props.mirror;
props->direction = src_props.direction;
}
// The normed string for the compound character is the concatenation of
// the normed versions of the individual characters.
props->normed += src_props.normed;
++total_unicodes;
}
if (total_unicodes > 1) {
// Estimate the total widths from the advance - bearing.
props->min_width = ClipToRange(props->min_advance - props->max_bearing,
-MAX_INT16, MAX_INT16);
props->max_width = ClipToRange(props->max_advance - props->min_bearing,
-MAX_INT16, MAX_INT16);
}
return total_unicodes > 0;
}
// Transform tesseract image coordinates to coordinates used in the projection.
int TextlineProjection::ImageXToProjectionX(int x) const {
x = ClipToRange((x - x_origin_) / scale_factor_, 0, pixGetWidth(pix_) - 1);
return x;
}
