void tess_training_tester( //call tess
const STRING& filename, //filename to output
PBLOB *blob, //blob to match
DENORM *denorm, //de-normaliser
BOOL8 correct, //ly segmented
char *text, //correct text
inT32 count, //chars in text
BLOB_CHOICE_LIST *ratings //list of results
) {
TBLOB *tessblob; //converted blob
TEXTROW tessrow; //dummy row
if (correct) {
classify_norm_method.set_value(character); // force char norm spc 30/11/93
tess_bn_matching.set_value(false); //turn it off
tess_cn_matching.set_value(false);
//convert blob
tessblob = make_tess_blob (blob, TRUE);
//make dummy row
make_tess_row(denorm, &tessrow);
//learn it
LearnBlob(filename, tessblob, &tessrow, text);
free_blob(tessblob);
}
}
/**********************************************************************
* compare_bln_blobs
*
* Compare 2 baseline normalised blobs and return the rating.
**********************************************************************/
float Tesseract::compare_bln_blobs( //match 2 blobs
PBLOB *blob1, //first blob
DENORM *denorm1,
PBLOB *blob2, //other blob
DENORM *denorm2) {
TBLOB *tblob1; //tessblobs
TBLOB *tblob2;
TEXTROW tessrow1, tessrow2; //tess rows
float rating; //match result
tblob1 = make_tess_blob (blob1, TRUE);
make_tess_row(denorm1, &tessrow1);
tblob2 = make_tess_blob (blob2, TRUE);
make_tess_row(denorm2, &tessrow2);
rating = compare_tess_blobs (tblob1, &tessrow1, tblob2, &tessrow2);
free_blob(tblob1);
free_blob(tblob2);
return rating;
}
void tess_adapter( //adapt to word
WERD *word, //bln word
DENORM *denorm, //de-normalise
const char *string, //string for word
const char *raw_string, //before context
const char *rejmap //reject map
) {
TWERD *tessword; //converted word
static TEXTROW tessrow; //dummy row
//make dummy row
make_tess_row(denorm, &tessrow);
//make a word
tessword = make_tess_word (word, &tessrow);
AdaptToWord(tessword, &tessrow, string, raw_string, rejmap);
//adapt to it
delete_word(tessword); //free it
}
void Tesseract::tess_bn_matcher( //call tess
PBLOB *pblob, //previous blob
PBLOB *blob, //blob to match
PBLOB *nblob, //next blob
WERD *word, //word it came from
DENORM *denorm, //de-normaliser
BLOB_CHOICE_LIST *ratings //list of results
) {
TBLOB *tessblob; //converted blob
TEXTROW tessrow; //dummy row
tess_bn_matching.set_value(true); //turn it on
tess_cn_matching.set_value(false);
//convert blob
tessblob = make_rotated_tess_blob(denorm, blob, true);
//make dummy row
make_tess_row(denorm, &tessrow);
//classify
AdaptiveClassifier(tessblob, NULL, &tessrow, ratings, NULL);
free_blob(tessblob);
}
void tess_training_tester( //call tess
PBLOB *blob, //blob to match
DENORM *denorm, //de-normaliser
BOOL8 correct, //ly segmented
char *text, //correct text
INT32 count, //chars in text
BLOB_CHOICE_LIST *ratings //list of results
) {
TBLOB *tessblob; //converted blob
TEXTROW tessrow; //dummy row
if (correct) {
NormMethod = character; //Force char norm spc 30/11/93
tess_bn_matching = FALSE; //turn it off
tess_cn_matching = FALSE;
//convert blob
tessblob = make_tess_blob (blob, TRUE);
//make dummy row
make_tess_row(denorm, &tessrow);
//learn it
LearnBlob(tessblob, &tessrow, text, count);
free_blob(tessblob);
}
}
void tess_default_matcher( //call tess
PBLOB *pblob, //previous blob
PBLOB *blob, //blob to match
PBLOB *nblob, //next blob
WERD *word, //word it came from
DENORM *denorm, //de-normaliser
BLOB_CHOICE_LIST &ratings //list of results
) {
LIST result; //tess output
TBLOB *tessblob; //converted blob
TEXTROW tessrow; //dummy row
tess_bn_matching = FALSE; //turn it off
tess_cn_matching = FALSE;
//convert blob
tessblob = make_tess_blob (blob, TRUE);
//make dummy row
make_tess_row(denorm, &tessrow);
//classify
result = AdaptiveClassifier (tessblob, NULL, &tessrow);
free_blob(tessblob);
//make our format
convert_choice_list(result, ratings);
}