BLOB_CHOICE* M_Utils::runBlobOCR(BLOBNBOX* blob, Tesseract* ocrengine) {
// * Normalize blob height to x-height (current OSD):
// SetupNormalization(NULL, NULL, &rotation, NULL, NULL, 0,
// box.rotational_x_middle(rotation),
// box.rotational_y_middle(rotation),
// kBlnXHeight / box.rotational_height(rotation),
// kBlnXHeight / box.rotational_height(rotation),
// 0, kBlnBaselineOffset);
BLOB_CHOICE_LIST ratings_lang;
C_BLOB* cblob = blob->cblob();
TBLOB* tblob = TBLOB::PolygonalCopy(cblob);
const TBOX& box = tblob->bounding_box();
// Normalize the blob. Set the origin to the place we want to be the
// bottom-middle, and scaling is to mpx, box_, NULL);
float scaling = static_cast<float>(kBlnXHeight) / box.height();
DENORM denorm;
float x_orig = (box.left() + box.right()) / 2.0f, y_orig = box.bottom();
denorm.SetupNormalization(NULL, NULL, NULL, NULL, NULL, 0,
x_orig, y_orig, scaling, scaling,
0.0f, static_cast<float>(kBlnBaselineOffset));
TBLOB* normed_blob = new TBLOB(*tblob);
normed_blob->Normalize(denorm);
ocrengine->AdaptiveClassifier(normed_blob, denorm, &ratings_lang, NULL);
delete normed_blob;
delete tblob;
// Get the best choice from ratings_lang and rating_equ. As the choice in the
// list has already been sorted by the certainty, we simply use the first
// choice.
BLOB_CHOICE *lang_choice = NULL;
if (ratings_lang.length() > 0) {
BLOB_CHOICE_IT choice_it(&ratings_lang);
lang_choice = choice_it.data();
}
return lang_choice;
}
// Normalizes the blob for classification only if needed.
// (Normally this means a non-zero classify rotation.)
// If no Normalization is needed, then NULL is returned, and the denorm is
// unchanged. Otherwise a new TBLOB is returned and the denorm points to
// a new DENORM. In this case, both the TBLOB and DENORM must be deleted.
TBLOB* TBLOB::ClassifyNormalizeIfNeeded(const DENORM** denorm) const {
TBLOB* rotated_blob = NULL;
// If necessary, copy the blob and rotate it. The rotation is always
// +/- 90 degrees, as 180 was already taken care of.
if ((*denorm)->block() != NULL &&
(*denorm)->block()->classify_rotation().y() != 0.0) {
TBOX box = bounding_box();
int x_middle = (box.left() + box.right()) / 2;
int y_middle = (box.top() + box.bottom()) / 2;
rotated_blob = new TBLOB(*this);
const FCOORD& rotation = (*denorm)->block()->classify_rotation();
DENORM* norm = new DENORM;
// Move the rotated blob back to the same y-position so that we
// can still distinguish similar glyphs with differeny y-position.
float target_y = kBlnBaselineOffset +
(rotation.y() > 0 ? x_middle - box.left() : box.right() - x_middle);
norm->SetupNormalization(NULL, NULL, &rotation, *denorm, NULL, 0,
x_middle, y_middle, 1.0f, 1.0f, 0.0f, target_y);
// x_middle, y_middle, 1.0f, 1.0f, 0.0f, y_middle);
rotated_blob->Normalize(*norm);
*denorm = norm;
}
return rotated_blob;
}
