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applybox.cpp, take one
This commit is contained in:
Jim O'Regan
parent
2982583daa
commit
192a24ab7b
@ -36,18 +36,22 @@
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#include "tesseractclass.h"
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#include "genericvector.h"
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// Max number of blobs to classify together in FindSegmentation.
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/** Max number of blobs to classify together in FindSegmentation. */
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const int kMaxGroupSize = 4;
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// Max fraction of median allowed as deviation in xheight before switching
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// to median.
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/// Max fraction of median allowed as deviation in xheight before switching
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/// to median.
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const double kMaxXHeightDeviationFraction = 0.125;
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/*************************************************************************
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/**
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* The box file is assumed to contain box definitions, one per line, of the
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* following format for blob-level boxes:
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* @verbatim
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* <UTF8 str> <left> <bottom> <right> <top> <page id>
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* @endverbatim
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* and for word/line-level boxes:
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* @verbatim
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* WordStr <left> <bottom> <right> <top> <page id> #<space-delimited word str>
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* @endverbatim
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* NOTES:
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* The boxes use tesseract coordinates, i.e. 0,0 is at BOTTOM-LEFT.
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*
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@ -62,13 +66,16 @@ const double kMaxXHeightDeviationFraction = 0.125;
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* units in the word/line are listed after the # at the end of the line and
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* are space delimited, ignoring any original spaces on the line.
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* Eg.
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* @verbatim
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* word -> #w o r d
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* multi word line -> #m u l t i w o r d l i n e
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* @endverbatim
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* The recognizable units must be space-delimited in order to allow multiple
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* unicodes to be used for a single recognizable unit, eg Hindi.
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*
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* In this mode, the classifier must have been pre-trained with the desired
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* character set, or it will not be able to find the character segmentations.
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*************************************************************************/
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*/
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namespace tesseract {
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@ -181,8 +188,8 @@ static double MedianXHeight(BLOCK_LIST *block_list) {
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return xheights.median();
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}
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// Any row xheight that is significantly different from the median is set
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// to the median.
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/// Any row xheight that is significantly different from the median is set
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/// to the median.
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void Tesseract::PreenXHeights(BLOCK_LIST *block_list) {
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double median_xheight = MedianXHeight(block_list);
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double max_deviation = kMaxXHeightDeviationFraction * median_xheight;
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@ -205,8 +212,8 @@ void Tesseract::PreenXHeights(BLOCK_LIST *block_list) {
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}
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}
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// Builds a PAGE_RES from the block_list in the way required for ApplyBoxes:
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// All fuzzy spaces are removed, and all the words are maximally chopped.
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/// Builds a PAGE_RES from the block_list in the way required for ApplyBoxes:
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/// All fuzzy spaces are removed, and all the words are maximally chopped.
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PAGE_RES* Tesseract::SetupApplyBoxes(const GenericVector<TBOX>& boxes,
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BLOCK_LIST *block_list) {
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PreenXHeights(block_list);
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@ -240,9 +247,9 @@ PAGE_RES* Tesseract::SetupApplyBoxes(const GenericVector<TBOX>& boxes,
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return page_res;
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}
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// Tests the chopper by exhaustively running chop_one_blob.
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// The word_res will contain filled chopped_word, seam_array, denorm,
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// box_word and best_state for the maximally chopped word.
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/// Tests the chopper by exhaustively running chop_one_blob.
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/// The word_res will contain filled chopped_word, seam_array, denorm,
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/// box_word and best_state for the maximally chopped word.
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void Tesseract::MaximallyChopWord(const GenericVector<TBOX>& boxes,
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BLOCK* block, ROW* row,
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WERD_RES* word_res) {
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@ -300,17 +307,17 @@ void Tesseract::MaximallyChopWord(const GenericVector<TBOX>& boxes,
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word_res->FakeClassifyWord(blob_choices.size(), &blob_choices[0]);
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}
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// Helper to compute the dispute resolution metric.
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// Disputed blob resolution. The aim is to give the blob to the most
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// appropriate boxfile box. Most of the time it is obvious, but if
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// two boxfile boxes overlap significantly it is not. If a small boxfile
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// box takes most of the blob, and a large boxfile box does too, then
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// we want the small boxfile box to get it, but if the small box
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// is much smaller than the blob, we don't want it to get it.
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// Details of the disputed blob resolution:
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// Given a box with area A, and a blob with area B, with overlap area C,
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// then the miss metric is (A-C)(B-C)/(AB) and the box with minimum
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// miss metric gets the blob.
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/// Helper to compute the dispute resolution metric.
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/// Disputed blob resolution. The aim is to give the blob to the most
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/// appropriate boxfile box. Most of the time it is obvious, but if
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/// two boxfile boxes overlap significantly it is not. If a small boxfile
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/// box takes most of the blob, and a large boxfile box does too, then
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/// we want the small boxfile box to get it, but if the small box
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/// is much smaller than the blob, we don't want it to get it.
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/// Details of the disputed blob resolution:
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/// Given a box with area A, and a blob with area B, with overlap area C,
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/// then the miss metric is (A-C)(B-C)/(AB) and the box with minimum
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/// miss metric gets the blob.
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static double BoxMissMetric(const TBOX& box1, const TBOX& box2) {
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int overlap_area = box1.intersection(box2).area();
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double miss_metric = box1.area()- overlap_area;
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@ -320,14 +327,16 @@ static double BoxMissMetric(const TBOX& box1, const TBOX& box2) {
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return miss_metric;
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}
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// Gather consecutive blobs that match the given box into the best_state
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// and corresponding correct_text.
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// Fights over which box owns which blobs are settled by pre-chopping and
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// applying the blobs to box or next_box with the least non-overlap.
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// Returns false if the box was in error, which can only be caused by
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// failing to find an appropriate blob for a box.
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// This means that occasionally, blobs may be incorrectly segmented if the
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// chopper fails to find a suitable chop point.
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/// Gather consecutive blobs that match the given box into the best_state
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/// and corresponding correct_text.
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///
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/// Fights over which box owns which blobs are settled by pre-chopping and
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/// applying the blobs to box or next_box with the least non-overlap.
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/// @return false if the box was in error, which can only be caused by
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/// failing to find an appropriate blob for a box.
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///
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/// This means that occasionally, blobs may be incorrectly segmented if the
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/// chopper fails to find a suitable chop point.
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bool Tesseract::ResegmentCharBox(PAGE_RES* page_res, const TBOX *prev_box,
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const TBOX& box, const TBOX& next_box,
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const char* correct_text) {
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@ -420,12 +429,12 @@ bool Tesseract::ResegmentCharBox(PAGE_RES* page_res, const TBOX *prev_box,
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return false; // Failure.
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}
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// Consume all source blobs that strongly overlap the given box,
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// putting them into a new word, with the correct_text label.
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// Fights over which box owns which blobs are settled by
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// applying the blobs to box or next_box with the least non-overlap.
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// Returns false if the box was in error, which can only be caused by
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// failing to find an overlapping blob for a box.
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/// Consume all source blobs that strongly overlap the given box,
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/// putting them into a new word, with the correct_text label.
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/// Fights over which box owns which blobs are settled by
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/// applying the blobs to box or next_box with the least non-overlap.
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/// @return false if the box was in error, which can only be caused by
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/// failing to find an overlapping blob for a box.
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bool Tesseract::ResegmentWordBox(BLOCK_LIST *block_list,
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const TBOX& box, const TBOX& next_box,
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const char* correct_text) {
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@ -495,8 +504,8 @@ bool Tesseract::ResegmentWordBox(BLOCK_LIST *block_list,
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return new_word != NULL;
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}
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// Resegments the words by running the classifier in an attempt to find the
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// correct segmentation that produces the required string.
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/// Resegments the words by running the classifier in an attempt to find the
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/// correct segmentation that produces the required string.
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void Tesseract::ReSegmentByClassification(PAGE_RES* page_res) {
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PAGE_RES_IT pr_it(page_res);
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WERD_RES* word_res;
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@ -521,8 +530,8 @@ void Tesseract::ReSegmentByClassification(PAGE_RES* page_res) {
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}
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}
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// Converts the space-delimited string of utf8 text to a vector of UNICHAR_ID.
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// Returns false if an invalid UNICHAR_ID is encountered.
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/// Converts the space-delimited string of utf8 text to a vector of UNICHAR_ID.
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/// @return false if an invalid UNICHAR_ID is encountered.
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bool Tesseract::ConvertStringToUnichars(const char* utf8,
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GenericVector<UNICHAR_ID>* class_ids) {
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for (int step = 0; *utf8 != '\0'; utf8 += step) {
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@ -541,12 +550,12 @@ bool Tesseract::ConvertStringToUnichars(const char* utf8,
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return true;
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}
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// Resegments the word to achieve the target_text from the classifier.
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// Returns false if the re-segmentation fails.
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// Uses brute-force combination of up to kMaxGroupSize adjacent blobs, and
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// applies a full search on the classifier results to find the best classified
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// segmentation. As a compromise to obtain better recall, 1-1 ambiguity
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// substitutions ARE used.
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/// Resegments the word to achieve the target_text from the classifier.
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/// Returns false if the re-segmentation fails.
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/// Uses brute-force combination of up to #kMaxGroupSize adjacent blobs, and
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/// applies a full search on the classifier results to find the best classified
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/// segmentation. As a compromise to obtain better recall, 1-1 ambiguity
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/// substitutions ARE used.
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bool Tesseract::FindSegmentation(const GenericVector<UNICHAR_ID>& target_text,
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WERD_RES* word_res) {
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// Classify all required combinations of blobs and save results in choices.
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@ -603,12 +612,12 @@ bool Tesseract::FindSegmentation(const GenericVector<UNICHAR_ID>& target_text,
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return true;
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}
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// Recursive helper to find a match to the target_text (from text_index
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// position) in the choices (from choices_pos position).
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// Choices is an array of GenericVectors, of length choices_length, with each
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// element representing a starting position in the word, and the
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// GenericVector holding classification results for a sequence of consecutive
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// blobs, with index 0 being a single blob, index 1 being 2 blobs etc.
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/// Recursive helper to find a match to the target_text (from text_index
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/// position) in the choices (from choices_pos position).
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/// @param choices is an array of GenericVectors, of length choices_length,
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/// with each element representing a starting position in the word, and the
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/// #GenericVector holding classification results for a sequence of consecutive
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/// blobs, with index 0 being a single blob, index 1 being 2 blobs etc.
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void Tesseract::SearchForText(const GenericVector<BLOB_CHOICE_LIST*>* choices,
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int choices_pos, int choices_length,
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const GenericVector<UNICHAR_ID>& target_text,
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@ -682,10 +691,10 @@ void Tesseract::SearchForText(const GenericVector<BLOB_CHOICE_LIST*>* choices,
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}
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}
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// Counts up the labelled words and the blobs within.
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// Deletes all unused or emptied words, counting the unused ones.
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// Resets W_BOL and W_EOL flags correctly.
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// Builds the rebuild_word and rebuilds the box_word and the best_choice.
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/// - Counts up the labelled words and the blobs within.
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/// - Deletes all unused or emptied words, counting the unused ones.
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/// - Resets W_BOL and W_EOL flags correctly.
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/// - Builds the rebuild_word and rebuilds the box_word and the best_choice.
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void Tesseract::TidyUp(PAGE_RES* page_res) {
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int ok_blob_count = 0;
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int bad_blob_count = 0;
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@ -743,7 +752,7 @@ void Tesseract::TidyUp(PAGE_RES* page_res) {
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}
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}
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// Logs a bad box by line in the box file and box coords.
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/** Logs a bad box by line in the box file and box coords.*/
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void Tesseract::ReportFailedBox(int boxfile_lineno, TBOX box,
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const char *box_ch, const char *err_msg) {
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tprintf("APPLY_BOXES: boxfile line %d/%s ((%d,%d),(%d,%d)): %s\n",
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@ -751,7 +760,7 @@ void Tesseract::ReportFailedBox(int boxfile_lineno, TBOX box,
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box.left(), box.bottom(), box.right(), box.top(), err_msg);
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}
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// Creates a fake best_choice entry in each WERD_RES with the correct text.
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/** Creates a fake best_choice entry in each WERD_RES with the correct text.*/
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void Tesseract::CorrectClassifyWords(PAGE_RES* page_res) {
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PAGE_RES_IT pr_it(page_res);
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for (WERD_RES *word_res = pr_it.word(); word_res != NULL;
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@ -774,8 +783,8 @@ void Tesseract::CorrectClassifyWords(PAGE_RES* page_res) {
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}
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}
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// Calls LearnWord to extract features for labelled blobs within each word.
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// Features are stored in an internal buffer.
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/// Calls #LearnWord to extract features for labelled blobs within each word.
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/// Features are stored in an internal buffer.
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void Tesseract::ApplyBoxTraining(const STRING& fontname, PAGE_RES* page_res) {
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PAGE_RES_IT pr_it(page_res);
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int word_count = 0;
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