mirror of
https://github.com/tesseract-ocr/tesseract.git
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284 lines
9.8 KiB
C++
284 lines
9.8 KiB
C++
/******************************************************************************
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** Filename: normmatch.c
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** Purpose: Simple matcher based on character normalization features.
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** Author: Dan Johnson
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** History: Wed Dec 19 16:18:06 1990, DSJ, Created.
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**
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** (c) Copyright Hewlett-Packard Company, 1988.
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** Licensed under the Apache License, Version 2.0 (the "License");
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** you may not use this file except in compliance with the License.
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** You may obtain a copy of the License at
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** http://www.apache.org/licenses/LICENSE-2.0
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** Unless required by applicable law or agreed to in writing, software
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** distributed under the License is distributed on an "AS IS" BASIS,
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** WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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** See the License for the specific language governing permissions and
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** limitations under the License.
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******************************************************************************/
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/*----------------------------------------------------------------------------
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Include Files and Type Defines
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----------------------------------------------------------------------------*/
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#include "normmatch.h"
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#include <stdio.h>
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#include <math.h>
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#include "classify.h"
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#include "clusttool.h"
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#include "const.h"
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#include "efio.h"
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#include "emalloc.h"
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#include "globals.h"
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#include "helpers.h"
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#include "normfeat.h"
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#include "scanutils.h"
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#include "unicharset.h"
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#include "params.h"
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struct NORM_PROTOS
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{
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int NumParams;
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PARAM_DESC *ParamDesc;
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LIST* Protos;
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int NumProtos;
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};
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/*----------------------------------------------------------------------------
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Private Function Prototypes
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----------------------------------------------------------------------------*/
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double NormEvidenceOf(register double NormAdj);
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void PrintNormMatch(FILE *File,
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int NumParams,
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PROTOTYPE *Proto,
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FEATURE Feature);
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NORM_PROTOS *ReadNormProtos(FILE *File);
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/*----------------------------------------------------------------------------
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Variables
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----------------------------------------------------------------------------*/
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/** control knobs used to control the normalization adjustment process */
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double_VAR(classify_norm_adj_midpoint, 32.0, "Norm adjust midpoint ...");
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double_VAR(classify_norm_adj_curl, 2.0, "Norm adjust curl ...");
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/** Weight of width variance against height and vertical position. */
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const double kWidthErrorWeighting = 0.125;
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/*----------------------------------------------------------------------------
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Public Code
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----------------------------------------------------------------------------*/
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/*---------------------------------------------------------------------------*/
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namespace tesseract {
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/**
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* This routine compares Features against each character
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* normalization proto for ClassId and returns the match
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* rating of the best match.
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* @param ClassId id of class to match against
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* @param feature character normalization feature
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* @param DebugMatch controls dump of debug info
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*
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* Globals:
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* #NormProtos character normalization prototypes
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*
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* @return Best match rating for Feature against protos of ClassId.
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* @note Exceptions: none
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* @note History: Wed Dec 19 16:56:12 1990, DSJ, Created.
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*/
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FLOAT32 Classify::ComputeNormMatch(CLASS_ID ClassId,
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const FEATURE_STRUCT& feature,
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BOOL8 DebugMatch) {
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LIST Protos;
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FLOAT32 BestMatch;
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FLOAT32 Match;
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FLOAT32 Delta;
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PROTOTYPE *Proto;
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int ProtoId;
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if (ClassId >= NormProtos->NumProtos) {
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ClassId = NO_CLASS;
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}
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/* handle requests for classification as noise */
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if (ClassId == NO_CLASS) {
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/* kludge - clean up constants and make into control knobs later */
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Match = (feature.Params[CharNormLength] *
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feature.Params[CharNormLength] * 500.0 +
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feature.Params[CharNormRx] *
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feature.Params[CharNormRx] * 8000.0 +
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feature.Params[CharNormRy] *
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feature.Params[CharNormRy] * 8000.0);
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return (1.0 - NormEvidenceOf (Match));
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}
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BestMatch = MAX_FLOAT32;
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Protos = NormProtos->Protos[ClassId];
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if (DebugMatch) {
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tprintf("\nChar norm for class %s\n", unicharset.id_to_unichar(ClassId));
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}
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ProtoId = 0;
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iterate(Protos) {
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Proto = (PROTOTYPE *) first_node (Protos);
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Delta = feature.Params[CharNormY] - Proto->Mean[CharNormY];
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Match = Delta * Delta * Proto->Weight.Elliptical[CharNormY];
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if (DebugMatch) {
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tprintf("YMiddle: Proto=%g, Delta=%g, Var=%g, Dist=%g\n",
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Proto->Mean[CharNormY], Delta,
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Proto->Weight.Elliptical[CharNormY], Match);
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}
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Delta = feature.Params[CharNormRx] - Proto->Mean[CharNormRx];
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Match += Delta * Delta * Proto->Weight.Elliptical[CharNormRx];
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if (DebugMatch) {
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tprintf("Height: Proto=%g, Delta=%g, Var=%g, Dist=%g\n",
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Proto->Mean[CharNormRx], Delta,
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Proto->Weight.Elliptical[CharNormRx], Match);
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}
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// Ry is width! See intfx.cpp.
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Delta = feature.Params[CharNormRy] - Proto->Mean[CharNormRy];
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if (DebugMatch) {
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tprintf("Width: Proto=%g, Delta=%g, Var=%g\n",
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Proto->Mean[CharNormRy], Delta,
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Proto->Weight.Elliptical[CharNormRy]);
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}
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Delta = Delta * Delta * Proto->Weight.Elliptical[CharNormRy];
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Delta *= kWidthErrorWeighting;
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Match += Delta;
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if (DebugMatch) {
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tprintf("Total Dist=%g, scaled=%g, sigmoid=%g, penalty=%g\n",
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Match, Match / classify_norm_adj_midpoint,
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NormEvidenceOf(Match), 256 * (1 - NormEvidenceOf(Match)));
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}
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if (Match < BestMatch)
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BestMatch = Match;
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ProtoId++;
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}
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return 1.0 - NormEvidenceOf(BestMatch);
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} /* ComputeNormMatch */
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void Classify::FreeNormProtos() {
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if (NormProtos != nullptr) {
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for (int i = 0; i < NormProtos->NumProtos; i++)
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FreeProtoList(&NormProtos->Protos[i]);
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Efree(NormProtos->Protos);
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Efree(NormProtos->ParamDesc);
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Efree(NormProtos);
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NormProtos = nullptr;
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}
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}
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} // namespace tesseract
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/*----------------------------------------------------------------------------
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Private Code
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----------------------------------------------------------------------------*/
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/**
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* @name NormEvidenceOf
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*
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* Return the new type of evidence number corresponding to this
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* normalization adjustment. The equation that represents the transform is:
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* 1 / (1 + (NormAdj / midpoint) ^ curl)
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*/
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double NormEvidenceOf(register double NormAdj) {
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NormAdj /= classify_norm_adj_midpoint;
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if (classify_norm_adj_curl == 3)
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NormAdj = NormAdj * NormAdj * NormAdj;
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else if (classify_norm_adj_curl == 2)
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NormAdj = NormAdj * NormAdj;
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else
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NormAdj = pow (NormAdj, classify_norm_adj_curl);
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return (1.0 / (1.0 + NormAdj));
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}
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/*---------------------------------------------------------------------------*/
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/**
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* This routine dumps out detailed normalization match info.
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* @param File open text file to dump match debug info to
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* @param NumParams # of parameters in proto and feature
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* @param Proto[] array of prototype parameters
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* @param Feature[] array of feature parameters
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* Globals: none
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* @return none
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* @note Exceptions: none
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* @note History: Wed Jan 2 09:49:35 1991, DSJ, Created.
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*/
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void PrintNormMatch(FILE *File,
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int NumParams,
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PROTOTYPE *Proto,
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FEATURE Feature) {
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int i;
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FLOAT32 ParamMatch;
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FLOAT32 TotalMatch;
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for (i = 0, TotalMatch = 0.0; i < NumParams; i++) {
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ParamMatch = (Feature->Params[i] - Mean(Proto, i)) /
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StandardDeviation(Proto, i);
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fprintf (File, " %6.1f", ParamMatch);
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if (i == CharNormY || i == CharNormRx)
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TotalMatch += ParamMatch * ParamMatch;
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}
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fprintf (File, " --> %6.1f (%4.2f)\n",
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TotalMatch, NormEvidenceOf (TotalMatch));
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} /* PrintNormMatch */
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/*---------------------------------------------------------------------------*/
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namespace tesseract {
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/**
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* This routine allocates a new data structure to hold
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* a set of character normalization protos. It then fills in
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* the data structure by reading from the specified File.
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* @param fp open text file to read normalization protos from
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* Globals: none
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* @return Character normalization protos.
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* @note Exceptions: none
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* @note History: Wed Dec 19 16:38:49 1990, DSJ, Created.
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*/
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NORM_PROTOS *Classify::ReadNormProtos(TFile *fp) {
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NORM_PROTOS *NormProtos;
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int i;
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char unichar[2 * UNICHAR_LEN + 1];
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UNICHAR_ID unichar_id;
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LIST Protos;
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int NumProtos;
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/* allocate and initialization data structure */
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NormProtos = (NORM_PROTOS *) Emalloc (sizeof (NORM_PROTOS));
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NormProtos->NumProtos = unicharset.size();
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NormProtos->Protos = (LIST *) Emalloc (NormProtos->NumProtos * sizeof(LIST));
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for (i = 0; i < NormProtos->NumProtos; i++)
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NormProtos->Protos[i] = NIL_LIST;
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/* read file header and save in data structure */
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NormProtos->NumParams = ReadSampleSize(fp);
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NormProtos->ParamDesc = ReadParamDesc(fp, NormProtos->NumParams);
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/* read protos for each class into a separate list */
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const int kMaxLineSize = 100;
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char line[kMaxLineSize];
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while (fp->FGets(line, kMaxLineSize) != nullptr) {
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if (sscanf(line, "%s %d", unichar, &NumProtos) != 2) continue;
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if (unicharset.contains_unichar(unichar)) {
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unichar_id = unicharset.unichar_to_id(unichar);
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Protos = NormProtos->Protos[unichar_id];
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for (i = 0; i < NumProtos; i++)
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Protos = push_last(Protos, ReadPrototype(fp, NormProtos->NumParams));
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NormProtos->Protos[unichar_id] = Protos;
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} else {
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tprintf("Error: unichar %s in normproto file is not in unichar set.\n",
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unichar);
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for (i = 0; i < NumProtos; i++)
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FreePrototype(ReadPrototype(fp, NormProtos->NumParams));
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}
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}
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return (NormProtos);
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} /* ReadNormProtos */
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} // namespace tesseract
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