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https://github.com/tesseract-ocr/tesseract.git
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This is the first draft of Tesseract API that is used by Ocropus.
git-svn-id: https://tesseract-ocr.googlecode.com/svn/trunk@103 d0cd1f9f-072b-0410-8dd7-cf729c803f20
This commit is contained in:
mezhirov
parent
e7c008cede
commit
024c9f49c0
@ -28,6 +28,18 @@
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#include "pgedit.h"
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#include "varabled.h"
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#include "output.h"
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#include "globals.h"
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#include "adaptmatch.h"
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#include "edgblob.h"
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#include "tessbox.h"
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#include "tordvars.h"
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#include "tessvars.h"
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#include "imgs.h"
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#include "makerow.h"
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#include "output.h"
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#include "tstruct.h"
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#include "tessout.h"
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#include "tface.h"
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#include "adaptmatch.h"
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BOOL_VAR(tessedit_resegment_from_boxes, FALSE,
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@ -675,3 +687,349 @@ char* TessBaseAPI::TesseractToUNLV(PAGE_RES* page_res) {
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return NULL;
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}
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// ____________________________________________________________________________
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// Find lines from the image making the BLOCK_LIST.
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BLOCK_LIST* TessBaseAPI::FindLinesCreateBlockList() {
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BLOCK_LIST *block_list = new BLOCK_LIST();
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FindLines(block_list);
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return block_list;
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}
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// Delete a block list.
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// This is to keep BLOCK_LIST pointer opaque
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// and let go of including the other headers.
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void TessBaseAPI::DeleteBlockList(BLOCK_LIST *block_list) {
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delete block_list;
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}
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static ROW *make_tess_ocrrow(float baseline,
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float xheight,
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float descender,
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float ascender) {
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INT32 xstarts[] = {-32000};
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double quad_coeffs[] = {0,0,baseline};
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return new ROW(
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1,
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xstarts,
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quad_coeffs,
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xheight,
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ascender - (baseline + xheight),
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descender - baseline,
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0,
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0
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);
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}
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// Almost a copy of make_tess_row() from ccmain/tstruct.cpp.
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static void fill_dummy_row(TEXTROW &tessrow, float baseline, float xheight, float descender, float ascender)
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{
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tessrow.baseline.segments = 1;
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tessrow.baseline.xstarts[0] = -32767;
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tessrow.baseline.xstarts[1] = 32767;
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tessrow.baseline.quads[0].a = 0;
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tessrow.baseline.quads[0].b = 0;
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tessrow.baseline.quads[0].c = bln_baseline_offset;
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tessrow.xheight.segments = 1;
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tessrow.xheight.xstarts[0] = -32767;
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tessrow.xheight.xstarts[1] = 32767;
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tessrow.xheight.quads[0].a = 0;
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tessrow.xheight.quads[0].b = 0;
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tessrow.xheight.quads[0].c = bln_baseline_offset + bln_x_height;
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tessrow.lineheight = bln_x_height;
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tessrow.ascrise = bln_x_height * (ascender - (xheight + baseline)) / xheight;
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tessrow.descdrop = bln_x_height * (descender - baseline) / xheight;
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}
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/// Return a TBLOB * from the whole page_image.
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/// To be freed later with free_blob().
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TBLOB *make_tesseract_blob(float baseline, float xheight, float descender, float ascender) {
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BLOCK *block = new BLOCK ("a character",
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TRUE,
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0, 0,
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0, 0,
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page_image.get_xsize(),
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page_image.get_ysize());
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// Create C_BLOBs from the page
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extract_edges(NULL, &page_image, &page_image,
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ICOORD(page_image.get_xsize(), page_image.get_ysize()),
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block);
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// Create one PBLOB from all C_BLOBs
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C_BLOB_LIST *list = block->blob_list();
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C_BLOB_IT c_blob_it(list);
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PBLOB *pblob = new PBLOB; // will be (hopefully) deleted by the pblob_list
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for (c_blob_it.mark_cycle_pt();
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!c_blob_it.cycled_list();
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c_blob_it.forward()) {
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C_BLOB *c_blob = c_blob_it.data();
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PBLOB c_as_p(c_blob, baseline + xheight);
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merge_blobs(pblob, &c_as_p);
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}
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PBLOB_LIST *pblob_list = new PBLOB_LIST; // will be deleted by the word
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PBLOB_IT pblob_it(pblob_list);
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pblob_it.add_after_then_move(pblob);
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// Normalize PBLOB
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WERD word(pblob_list, 0, " ");
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ROW *row = make_tess_ocrrow(baseline, xheight, descender, ascender);
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word.baseline_normalise(row);
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delete row;
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// Create a TBLOB from PBLOB
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return make_tess_blob(pblob, /* flatten: */ TRUE);
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}
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// Adapt to recognize the current image as the given character.
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// The image must be preloaded and be just an image of a single character.
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void TessBaseAPI::AdaptToCharacter(const char *unichar_repr,
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int length,
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float baseline,
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float xheight,
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float descender,
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float ascender) {
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UNICHAR_ID id = unicharset.unichar_to_id(unichar_repr, length);
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LINE_STATS LineStats;
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TEXTROW row;
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fill_dummy_row(row, baseline, xheight, descender, ascender);
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GetLineStatsFromRow(&row, &LineStats);
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TBLOB *blob = make_tesseract_blob(baseline, xheight, descender, ascender);
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float threshold;
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int best_class = 0;
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float best_rating = -100;
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// Classify to get a raw choice.
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LIST result = AdaptiveClassifier(blob, NULL, &row);
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LIST p;
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for (p = result; p != NULL; p = p->next) {
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A_CHOICE *tesschoice = (A_CHOICE *) p->node;
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if (tesschoice->rating > best_rating) {
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best_rating = tesschoice->rating;
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best_class = tesschoice->string[0];
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}
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}
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FLOAT32 GetBestRatingFor(TBLOB *Blob, LINE_STATS *LineStats, CLASS_ID ClassId);
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// We have to use char-level adaptation because otherwise
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// someone should do forced alignment somewhere.
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void AdaptToChar(TBLOB *Blob,
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LINE_STATS *LineStats,
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CLASS_ID ClassId,
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FLOAT32 Threshold);
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if (id == best_class)
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threshold = GoodAdaptiveMatch;
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else {
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/* the blob was incorrectly classified - find the rating threshold
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needed to create a template which will correct the error with
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some margin. However, don't waste time trying to make
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templates which are too tight. */
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threshold = GetBestRatingFor(blob, &LineStats, id);
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threshold *= .9;
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const float max_threshold = .125;
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const float min_threshold = .02;
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if (threshold > max_threshold)
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threshold = max_threshold;
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// I have cuddled the following line to set it out of the strike
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// of the coverage testing tool. I have no idea how to trigger
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// this situation nor I have any necessity to do it. --mezhirov
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if (threshold < min_threshold) threshold = min_threshold;
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}
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if (blob->outlines)
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AdaptToChar(blob, &LineStats, id, threshold);
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free_blob(blob);
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}
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PAGE_RES* TessBaseAPI::RecognitionPass1(BLOCK_LIST* block_list) {
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PAGE_RES *page_res = new PAGE_RES(block_list);
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recog_all_words(page_res, NULL, NULL, 1);
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return page_res;
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}
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PAGE_RES* TessBaseAPI::RecognitionPass2(BLOCK_LIST* block_list, PAGE_RES* pass1_result) {
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if (!pass1_result)
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pass1_result = new PAGE_RES(block_list);
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recog_all_words(pass1_result, NULL, NULL, 2);
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return pass1_result;
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}
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// brief Get a bounding box of a PBLOB.
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static BOX pblob_get_bbox(PBLOB *blob) {
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OUTLINE_LIST *outlines = blob->out_list();
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OUTLINE_IT it(outlines);
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BOX result;
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for (it.mark_cycle_pt(); !it.cycled_list(); it.forward()) {
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OUTLINE *outline = it.data();
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outline->compute_bb();
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result.bounding_union(outline->bounding_box());
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}
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return result;
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}
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static BOX c_blob_list_get_bbox(C_BLOB_LIST *cblobs) {
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BOX result;
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C_BLOB_IT c_it(cblobs);
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for (c_it.mark_cycle_pt(); !c_it.cycled_list(); c_it.forward()) {
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C_BLOB *blob = c_it.data();
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//bboxes.push(tessy_rectangle(blob->bounding_box()));
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result.bounding_union(blob->bounding_box());
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}
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}
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struct TESS_CHAR : ELIST_LINK {
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char *unicode_repr;
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int length; // of unicode_repr
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float cost;
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BOX box;
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TESS_CHAR(float _cost, const char *repr, int len = -1) : cost(_cost) {
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length = (len == -1 ? strlen(repr) : len);
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unicode_repr = new char[length + 1];
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strncpy(unicode_repr, repr, length);
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}
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~TESS_CHAR() {
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delete unicode_repr;
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}
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};
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static void add_space(ELIST_ITERATOR *it) {
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TESS_CHAR *t = new TESS_CHAR(0, " ");
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it->add_after_then_move(t);
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}
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static float rating_to_cost(float rating) {
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rating = 100 + rating;
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// cuddled that to save from coverage profiler
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// (I have never seen ratings worse than -100,
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// but the check won't hurt)
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if (rating < 0) rating = 0;
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return rating;
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}
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// Extract the OCR results, costs (penalty points for uncertainty),
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// and the bounding boxes of the characters.
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static void extract_result(ELIST_ITERATOR *out,
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PAGE_RES* page_res) {
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PAGE_RES_IT page_res_it(page_res);
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int word_count = 0;
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while (page_res_it.word() != NULL) {
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WERD_RES *word = page_res_it.word();
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const char *str = word->best_choice->string().string();
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const char *len = word->best_choice->lengths().string();
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if (word_count)
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add_space(out);
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BOX bln_rect;
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PBLOB_LIST *blobs = word->outword->blob_list();
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PBLOB_IT it(blobs);
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int n = strlen(len);
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BOX *(boxes_to_fix[n]);
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for (int i = 0; i < n; i++) {
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PBLOB *blob = it.data();
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BOX current = pblob_get_bbox(blob);
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bln_rect.bounding_union(current);
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TESS_CHAR *tc = new TESS_CHAR(
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rating_to_cost(word->best_choice->rating()),
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str, *len);
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tc->box = current;
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boxes_to_fix[i] = &tc->box;
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out->add_after_then_move(tc);
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it.forward();
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str += *len;
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len++;
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}
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// Find the word bbox before normalization.
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// Here we can't use the C_BLOB bboxes directly,
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// since connected letters are not yet cut.
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BOX real_rect = c_blob_list_get_bbox(word->word->cblob_list());
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// Denormalize boxes by transforming the bbox of the whole bln word
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// into the denorm bbox (`real_rect') of the whole word.
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double x_stretch = double(real_rect.width()) / bln_rect.width();
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double y_stretch = double(real_rect.height()) / bln_rect.height();
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for (int i = 0; i < n; i++) {
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BOX *box = boxes_to_fix[i];
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int x0 = int(real_rect.left() +
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x_stretch * (box->left() - bln_rect.left()) + 0.5);
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int x1 = int(real_rect.left() +
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x_stretch * (box->right() - bln_rect.left()) + 0.5);
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int y0 = int(real_rect.bottom() +
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y_stretch * (box->bottom() - bln_rect.bottom()) + 0.5);
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int y1 = int(real_rect.bottom() +
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y_stretch * (box->top() - bln_rect.bottom()) + 0.5);
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*box = BOX(ICOORD(x0, y0), ICOORD(x1, y1));
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}
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page_res_it.forward();
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word_count++;
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}
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}
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// Extract the OCR results, costs (penalty points for uncertainty),
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// and the bounding boxes of the characters.
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int TessBaseAPI::TesseractExtractResult(char** string,
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int** lengths,
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float** costs,
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int** x0,
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int** y0,
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int** x1,
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int** y1,
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PAGE_RES* page_res) {
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ELIST tess_chars;
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ELIST_ITERATOR tess_chars_it(&tess_chars);
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extract_result(&tess_chars_it, page_res);
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tess_chars_it.move_to_first();
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int n = tess_chars.length();
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int string_len = 0;
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*lengths = new int[n];
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*costs = new float[n];
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*x0 = new int[n];
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*y0 = new int[n];
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*x1 = new int[n];
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*y1 = new int[n];
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int i = 0;
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for (tess_chars_it.mark_cycle_pt();
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!tess_chars_it.cycled_list();
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tess_chars_it.forward(), i++)
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{
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TESS_CHAR *tc = (TESS_CHAR *) tess_chars_it.data();
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string_len += (*lengths)[i] = tc->length;
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(*costs)[i] = tc->cost;
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(*x0)[i] = tc->box.left();
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(*y0)[i] = tc->box.bottom();
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(*x1)[i] = tc->box.right();
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(*y1)[i] = tc->box.top();
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}
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char *p = *string = new char[string_len];
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tess_chars_it.move_to_first();
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for (tess_chars_it.mark_cycle_pt();
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!tess_chars_it.cycled_list();
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tess_chars_it.forward())
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{
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TESS_CHAR *tc = (TESS_CHAR *) tess_chars_it.data();
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strncpy(p, tc->unicode_repr, tc->length);
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p += tc->length;
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}
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return n;
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}
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@ -180,6 +180,40 @@ class TessBaseAPI {
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// The input page_res is deleted. The text string is converted
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// to UNLV-format: Latin-1 with specific reject and suspect codes.
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static char* TesseractToUNLV(PAGE_RES* page_res);
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// __________________________ ocropus add-ons ___________________________
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// Find lines from the image making the BLOCK_LIST.
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static BLOCK_LIST* FindLinesCreateBlockList();
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// Delete a block list.
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// This is to keep BLOCK_LIST pointer opaque
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// and let go of including the other headers.
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static void DeleteBlockList(BLOCK_LIST *);
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// Adapt to recognize the current image as the given character.
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// The image must be preloaded and be just an image of a single character.
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static void AdaptToCharacter(const char *unichar_repr,
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int length,
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float baseline,
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float xheight,
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float descender,
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float ascender);
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// Recognize text doing one pass only, using settings for a given pass.
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static PAGE_RES* RecognitionPass1(BLOCK_LIST* block_list);
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static PAGE_RES* RecognitionPass2(BLOCK_LIST* block_list, PAGE_RES* pass1_result);
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// Extract the OCR results, costs (penalty points for uncertainty),
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// and the bounding boxes of the characters.
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static int TesseractExtractResult(char** string,
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int** lengths,
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float** costs,
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int** x0,
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int** y0,
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int** x1,
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int** y1,
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PAGE_RES* page_res);
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};
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#endif // THIRD_PARTY_TESSERACT_CCMAIN_BASEAPI_H__
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