This fixes these compiler warnings:
ccmain/equationdetect.cpp:1519:2: warning: extra ‘;’ [-Wpedantic]
ccstruct/blobs.cpp:65:17: warning: extra ‘;’ [-Wpedantic]
ccstruct/blobs.h:178:18: warning: extra ‘;’ [-Wpedantic]
ccstruct/ratngs.cpp:36:22: warning: extra ‘;’ [-Wpedantic]
ccstruct/ratngs.cpp:37:22: warning: extra ‘;’ [-Wpedantic]
ccutil/ambigs.cpp:46:20: warning: extra ‘;’ [-Wpedantic]
ccutil/ambigs.h:137:21: warning: extra ‘;’ [-Wpedantic]
cutil/structures.cpp:36:45: warning: extra ‘;’ [-Wpedantic]
textord/equationdetectbase.cpp:65:2: warning: extra ‘;’ [-Wpedantic]
textord/equationdetectbase.h:57:2: warning: extra ‘;’ [-Wpedantic]
wordrec/lm_state.cpp:25:28: warning: extra ‘;’ [-Wpedantic]
wordrec/lm_state.h:190:29: warning: extra ‘;’ [-Wpedantic]
Signed-off-by: Stefan Weil <sw@weilnetz.de>
gcc reports these warnings with -Wextra:
ccstruct/pageres.h:330:3: warning:
base class 'class ELIST_LINK' should be explicitly initialized
in the copy constructor [-Wextra]
ccstruct/ratngs.cpp:115:1: warning:
base class 'class ELIST_LINK' should be explicitly initialized
in the copy constructor [-Wextra]
ccstruct/ratngs.h:291:3: warning:
base class 'class ELIST_LINK' should be explicitly initialized
in the copy constructor [-Wextra]
ccutil/genericvector.h:435:3: warning:
base class 'class GenericVector<WERD_RES*>' should be explicitly initialized
in the copy constructor [-Wextra]
Signed-off-by: Stefan Weil <sw@weilnetz.de>
Font recognition was poor, due to forcing a 1st and 2nd choice at
a character level, when the total score for the correct font is often
correct at the word level, so allowed the propagation of a full set
of fonts and scores to the word recognizer, which can now decide word
level fonts using the scores instead of simple votes.
Change precipitated a cleanup of output data structures for classifier
results, eliminating ScoredClass and INT_RESULT_STRUCT, with a few
extra elements going in UnicharRating, and using that wherever possible.
That added the extra complexity of 1-rating due to a flip between 0 is
good and 0 is bad for the internal classifier scores before they are
converted to rating and certainty.
