/usr/include/leptonica/recog.h is in libleptonica-dev 1.75.3-3.
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- Copyright (C) 2001 Leptonica. All rights reserved.
-
- Redistribution and use in source and binary forms, with or without
- modification, are permitted provided that the following conditions
- are met:
- 1. Redistributions of source code must retain the above copyright
- notice, this list of conditions and the following disclaimer.
- 2. Redistributions in binary form must reproduce the above
- copyright notice, this list of conditions and the following
- disclaimer in the documentation and/or other materials
- provided with the distribution.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
- ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
- LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
- A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL ANY
- CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
- EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
- PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
- PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
- OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
- NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*====================================================================*/
#ifndef LEPTONICA_RECOG_H
#define LEPTONICA_RECOG_H
/*!
* \file recog.h
*
* <pre>
* This is a simple utility for training and recognizing individual
* machine-printed text characters. It is designed to be adapted
* to a particular set of character images; e.g., from a book.
*
* There are two methods of training the recognizer. In the most
* simple, a set of bitmaps has been labeled by some means, such
* a generic OCR program. This is input either one template at a time
* or as a pixa of templates, to a function that creates a recog.
* If in a pixa, the text string label must be embedded in the
* text field of each pix.
*
* If labeled data is not available, we start with a bootstrap
* recognizer (BSR) that has labeled data from a variety of sources.
* These images are scaled, typically to a fixed height, and then
* fed similarly scaled unlabeled images from the source (e.g., book),
* and the BSR attempts to identify them. All images that have
* a high enough correlation score with one of the templates in the
* BSR are emitted in a pixa, which now holds unscaled and labeled
* templates from the source. This is the generator for a book adapted
* recognizer (BAR).
*
* The pixa should always be thought of as the primary structure.
* It is the generator for the recog, because a recog is built
* from a pixa of unscaled images.
*
* New image templates can be added to a recog as long as it is
* in training mode. Once training is finished, to add templates
* it is necessary to extract the generating pixa, add templates
* to that pixa, and make a new recog. Similarly, we do not
* join two recog; instead, we simply join their generating pixa,
* and make a recog from that.
*
* To remove outliers from a pixa of labeled pix, make a recog,
* determine the outliers, and generate a new pixa with the
* outliers removed. The outliers are determined by building
* special templates for each character set that are scaled averages
* of the individual templates. Then a correlation score is found
* between each template and the averaged templates. There are
* two implementations; outliers are determined as either:
* (1) a template having a correlation score with its class average
* that is below a threshold, or
* (2) a template having a correlation score with its class average
* that is smaller than the correlation score with the average
* of another class.
* Outliers are removed from the generating pixa. Scaled averaging
* is only performed for determining outliers and for splitting
* characters; it is never used in a trained recognizer for identifying
* unlabeled samples.
*
* Two methods using averaged templates are provided for splitting
* touching characters:
* (1) greedy matching
* (2) document image decoding (DID)
* The DID method is the default. It is about 5x faster and
* possibly more accurate.
*
* Once a BAR has been made, unlabeled sample images are identified
* by finding the individual template in the BAR with highest
* correlation. The input images and images in the BAR can be
* represented in two ways:
* (1) as scanned, binarized to 1 bpp
* (2) as a width-normalized outline formed by thinning to a
* skeleton and then dilating by a fixed amount.
*
* The recog can be serialized to file and read back. The serialized
* version holds the templates used for correlation (which may have
* been modified by scaling and turning into lines from the unscaled
* templates), plus, for arbitrary character sets, the UTF8
* representation and the lookup table mapping from the character
* representation to index.
*
* Why do we not use averaged templates for recognition?
* Letterforms can take on significantly different shapes (eg.,
* the letters 'a' and 'g'), and it makes no sense to average these.
* The previous version of this utility allowed multiple recognizers
* to exist, but this is an unnecessary complication if recognition
* is done on all samples instead of on averages.
* </pre>
*/
#define RECOG_VERSION_NUMBER 2
struct L_Recog {
l_int32 scalew; /*!< scale all examples to this width; */
/*!< use 0 prevent horizontal scaling */
l_int32 scaleh; /*!< scale all examples to this height; */
/*!< use 0 prevent vertical scaling */
l_int32 linew; /*!< use a value > 0 to convert the bitmap */
/*!< to lines of fixed width; 0 to skip */
l_int32 templ_use; /*!< template use: use either the average */
/*!< or all temmplates (L_USE_AVERAGE or */
/*!< L_USE_ALL) */
l_int32 maxarraysize; /*!< initialize container arrays to this */
l_int32 setsize; /*!< size of character set */
l_int32 threshold; /*!< for binarizing if depth > 1 */
l_int32 maxyshift; /*!< vertical jiggle on nominal centroid */
/*!< alignment; typically 0 or 1 */
l_int32 charset_type; /*!< one of L_ARABIC_NUMERALS, etc. */
l_int32 charset_size; /*!< expected number of classes in charset */
l_int32 min_nopad; /*!< min number of samples without padding */
l_int32 num_samples; /*!< number of training samples */
l_int32 minwidth_u; /*!< min width averaged unscaled templates */
l_int32 maxwidth_u; /*!< max width averaged unscaled templates */
l_int32 minheight_u; /*!< min height averaged unscaled templates */
l_int32 maxheight_u; /*!< max height averaged unscaled templates */
l_int32 minwidth; /*!< min width averaged scaled templates */
l_int32 maxwidth; /*!< max width averaged scaled templates */
l_int32 ave_done; /*!< set to 1 when averaged bitmaps are made */
l_int32 train_done; /*!< set to 1 when training is complete or */
/*!< identification has started */
l_float32 max_wh_ratio; /*!< max width/height ratio to split */
l_float32 max_ht_ratio; /*!< max of max/min template height ratio */
l_int32 min_splitw; /*!< min component width kept in splitting */
l_int32 max_splith; /*!< max component height kept in splitting */
struct Sarray *sa_text; /*!< text array for arbitrary char set */
struct L_Dna *dna_tochar; /*!< index-to-char lut for arbitrary charset */
l_int32 *centtab; /*!< table for finding centroids */
l_int32 *sumtab; /*!< table for finding pixel sums */
struct Pixaa *pixaa_u; /*!< all unscaled templates for each class */
struct Ptaa *ptaa_u; /*!< centroids of all unscaled templates */
struct Numaa *naasum_u; /*!< area of all unscaled templates */
struct Pixaa *pixaa; /*!< all (scaled) templates for each class */
struct Ptaa *ptaa; /*!< centroids of all (scaledl) templates */
struct Numaa *naasum; /*!< area of all (scaled) templates */
struct Pixa *pixa_u; /*!< averaged unscaled templates per class */
struct Pta *pta_u; /*!< centroids of unscaled ave. templates */
struct Numa *nasum_u; /*!< area of unscaled averaged templates */
struct Pixa *pixa; /*!< averaged (scaled) templates per class */
struct Pta *pta; /*!< centroids of (scaled) ave. templates */
struct Numa *nasum; /*!< area of (scaled) averaged templates */
struct Pixa *pixa_tr; /*!< all input training images */
struct Pixa *pixadb_ave; /*!< unscaled and scaled averaged bitmaps */
struct Pixa *pixa_id; /*!< input images for identifying */
struct Pix *pixdb_ave; /*!< debug: best match of input against ave. */
struct Pix *pixdb_range; /*!< debug: best matches within range */
struct Pixa *pixadb_boot; /*!< debug: bootstrap training results */
struct Pixa *pixadb_split; /*!< debug: splitting results */
struct L_Bmf *bmf; /*!< bmf fonts */
l_int32 bmf_size; /*!< font size of bmf; default is 6 pt */
struct L_Rdid *did; /*!< temp data used for image decoding */
struct L_Rch *rch; /*!< temp data used for holding best char */
struct L_Rcha *rcha; /*!< temp data used for array of best chars */
};
typedef struct L_Recog L_RECOG;
/*!
* Data returned from correlation matching on a single character
*/
struct L_Rch {
l_int32 index; /*!< index of best template */
l_float32 score; /*!< correlation score of best template */
char *text; /*!< character string of best template */
l_int32 sample; /*!< index of best sample (within the best */
/*!< template class, if all samples are used) */
l_int32 xloc; /*!< x-location of template (delx + shiftx) */
l_int32 yloc; /*!< y-location of template (dely + shifty) */
l_int32 width; /*!< width of best template */
};
typedef struct L_Rch L_RCH;
/*!
* Data returned from correlation matching on an array of characters
*/
struct L_Rcha {
struct Numa *naindex; /*!< indices of best templates */
struct Numa *nascore; /*!< correlation scores of best templates */
struct Sarray *satext; /*!< character strings of best templates */
struct Numa *nasample; /*!< indices of best samples */
struct Numa *naxloc; /*!< x-locations of templates (delx + shiftx) */
struct Numa *nayloc; /*!< y-locations of templates (dely + shifty) */
struct Numa *nawidth; /*!< widths of best templates */
};
typedef struct L_Rcha L_RCHA;
/*!
* Data used for decoding a line of characters.
*/
struct L_Rdid {
struct Pix *pixs; /*!< clone of pix to be decoded */
l_int32 **counta; /*!< count array for each averaged template */
l_int32 **delya; /*!< best y-shift array per average template */
l_int32 narray; /*!< number of averaged templates */
l_int32 size; /*!< size of count array (width of pixs) */
l_int32 *setwidth; /*!< setwidths for each template */
struct Numa *nasum; /*!< pixel count in pixs by column */
struct Numa *namoment; /*!< first moment of pixels in pixs by cols */
l_int32 fullarrays; /*!< 1 if full arrays are made; 0 otherwise */
l_float32 *beta; /*!< channel coeffs for template fg term */
l_float32 *gamma; /*!< channel coeffs for bit-and term */
l_float32 *trellisscore; /*!< score on trellis */
l_int32 *trellistempl; /*!< template on trellis (for backtrack) */
struct Numa *natempl; /*!< indices of best path templates */
struct Numa *naxloc; /*!< x locations of best path templates */
struct Numa *nadely; /*!< y locations of best path templates */
struct Numa *nawidth; /*!< widths of best path templates */
struct Boxa *boxa; /*!< Viterbi result for splitting input pixs */
struct Numa *nascore; /*!< correlation scores: best path templates */
struct Numa *natempl_r; /*!< indices of best rescored templates */
struct Numa *nasample_r; /*!< samples of best scored templates */
struct Numa *naxloc_r; /*!< x locations of best rescoredtemplates */
struct Numa *nadely_r; /*!< y locations of best rescoredtemplates */
struct Numa *nawidth_r; /*!< widths of best rescoredtemplates */
struct Numa *nascore_r; /*!< correlation scores: rescored templates */
};
typedef struct L_Rdid L_RDID;
/*-------------------------------------------------------------------------*
* Flags for describing limited character sets *
*-------------------------------------------------------------------------*/
/*! Flags for describing limited character sets */
enum {
L_UNKNOWN = 0, /*!< character set type is not specified */
L_ARABIC_NUMERALS = 1, /*!< 10 digits */
L_LC_ROMAN_NUMERALS = 2, /*!< 7 lower-case letters (i,v,x,l,c,d,m) */
L_UC_ROMAN_NUMERALS = 3, /*!< 7 upper-case letters (I,V,X,L,C,D,M) */
L_LC_ALPHA = 4, /*!< 26 lower-case letters */
L_UC_ALPHA = 5 /*!< 26 upper-case letters */
};
/*-------------------------------------------------------------------------*
* Flags for selecting between using average and all templates *
*-------------------------------------------------------------------------*/
/*! Flags for selecting average or all templates: recog->templ_use */
enum {
L_USE_ALL_TEMPLATES = 0, /*!< use all templates; default */
L_USE_AVERAGE_TEMPLATES = 1 /*!< use average templates; special cases */
};
#endif /* LEPTONICA_RECOG_H */
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