mirror of
https://github.com/tesseract-ocr/tesseract.git
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530 lines
19 KiB
Bash
Executable File
530 lines
19 KiB
Bash
Executable File
#!/bin/bash
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# (C) Copyright 2014, Google Inc.
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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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# This script defines functions that are used by tesstrain.sh
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# For a detailed description of the phases, see
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# https://github.com/tesseract-ocr/tesseract/wiki/TrainingTesseract
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#
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# USAGE: source tesstrain_utils.sh
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if [ "$(uname)" == "Darwin" ];then
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FONTS_DIR="/Library/Fonts/"
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else
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FONTS_DIR="/usr/share/fonts/"
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fi
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OUTPUT_DIR="/tmp/tesstrain/tessdata"
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OVERWRITE=0
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RUN_SHAPE_CLUSTERING=0
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EXTRACT_FONT_PROPERTIES=1
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WORKSPACE_DIR=`mktemp -d`
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# Logging helper functions.
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tlog() {
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echo -e $* 2>&1 1>&2 | tee -a ${LOG_FILE}
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}
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err_exit() {
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echo -e "ERROR: "$* 2>&1 1>&2 | tee -a ${LOG_FILE}
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exit 1
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}
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# Helper function to run a command and append its output to a log. Aborts early
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# if the program file is not found.
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# Usage: run_command CMD ARG1 ARG2...
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run_command() {
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local cmd=`which $1`
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if [[ -z ${cmd} ]]; then
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err_exit "$1 not found"
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fi
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shift
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tlog "[$(date)] ${cmd} $@"
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${cmd} "$@" 2>&1 1>&2 | tee -a ${LOG_FILE}
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# check completion status
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if [[ $? -gt 0 ]]; then
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err_exit "Program $(basename ${cmd}) failed. Abort."
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fi
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}
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# Check if all the given files exist, or exit otherwise.
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# Used to check required input files and produced output files in each phase.
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# Usage: check_file_readable FILE1 FILE2...
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check_file_readable() {
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for file in $@; do
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if [[ ! -r ${file} ]]; then
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err_exit "${file} does not exist or is not readable"
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fi
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done
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}
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# Sets the named variable to given value. Aborts if the value is missing or
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# if it looks like a flag.
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# Usage: parse_value VAR_NAME VALUE
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parse_value() {
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local val="$2"
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if [[ -z $val ]]; then
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err_exit "Missing value for variable $1"
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exit
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fi
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if [[ ${val:0:2} == "--" ]]; then
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err_exit "Invalid value $val passed for variable $1"
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exit
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fi
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eval $1=\"$val\"
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}
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# Does simple command-line parsing and initialization.
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parse_flags() {
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local i=0
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while test $i -lt ${#ARGV[@]}; do
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local j=$((i+1))
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case ${ARGV[$i]} in
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--)
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break;;
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--fontlist)
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fn=0
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FONTS=""
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while test $j -lt ${#ARGV[@]}; do
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test -z "${ARGV[$j]}" && break
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test `echo ${ARGV[$j]} | cut -c -2` = "--" && break
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FONTS[$fn]="${ARGV[$j]}"
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fn=$((fn+1))
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j=$((j+1))
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done
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i=$((j-1)) ;;
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--exposures)
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exp=""
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while test $j -lt ${#ARGV[@]}; do
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test -z "${ARGV[$j]}" && break
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test `echo ${ARGV[$j]} | cut -c -2` = "--" && break
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exp="$exp ${ARGV[$j]}"
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j=$((j+1))
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done
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parse_value "EXPOSURES" "$exp"
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i=$((j-1)) ;;
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--fonts_dir)
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parse_value "FONTS_DIR" ${ARGV[$j]}
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i=$j ;;
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--lang)
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parse_value "LANG_CODE" ${ARGV[$j]}
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i=$j ;;
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--langdata_dir)
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parse_value "LANGDATA_ROOT" ${ARGV[$j]}
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i=$j ;;
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--output_dir)
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parse_value "OUTPUT_DIR" ${ARGV[$j]}
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i=$j ;;
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--overwrite)
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OVERWRITE=1 ;;
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--extract_font_properties)
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EXTRACT_FONT_PROPERTIES=1 ;;
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--noextract_font_properties)
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EXTRACT_FONT_PROPERTIES=0 ;;
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--tessdata_dir)
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parse_value "TESSDATA_DIR" ${ARGV[$j]}
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i=$j ;;
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--training_text)
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parse_value "TRAINING_TEXT" "${ARGV[$j]}"
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i=$j ;;
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--wordlist)
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parse_value "WORDLIST_FILE" ${ARGV[$j]}
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i=$j ;;
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*)
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err_exit "Unrecognized argument ${ARGV[$i]}" ;;
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esac
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i=$((i+1))
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done
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if [[ -z ${LANG_CODE} ]]; then
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err_exit "Need to specify a language --lang"
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fi
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if [[ -z ${LANGDATA_ROOT} ]]; then
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err_exit "Need to specify path to language files --langdata_dir"
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fi
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if [[ -z ${TESSDATA_DIR} ]]; then
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if [[ -z ${TESSDATA_PREFIX} ]]; then
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err_exit "Need to specify a --tessdata_dir or have a "\
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"TESSDATA_PREFIX variable defined in your environment"
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else
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TESSDATA_DIR="${TESSDATA_PREFIX}"
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fi
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fi
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# Location where intermediate files will be created.
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TRAINING_DIR=${WORKSPACE_DIR}/${LANG_CODE}
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# Location of log file for the whole run.
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LOG_FILE=${TRAINING_DIR}/tesstrain.log
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# Take training text and wordlist from the langdata directory if not
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# specified in the command-line.
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if [[ -z ${TRAINING_TEXT} ]]; then
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TRAINING_TEXT=${LANGDATA_ROOT}/${LANG_CODE}/${LANG_CODE}.training_text
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fi
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if [[ -z ${WORDLIST_FILE} ]]; then
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WORDLIST_FILE=${LANGDATA_ROOT}/${LANG_CODE}/${LANG_CODE}.wordlist
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fi
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WORD_BIGRAMS_FILE=${LANGDATA_ROOT}/${LANG_CODE}/${LANG_CODE}.word.bigrams
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NUMBERS_FILE=${LANGDATA_ROOT}/${LANG_CODE}/${LANG_CODE}.numbers
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PUNC_FILE=${LANGDATA_ROOT}/${LANG_CODE}/${LANG_CODE}.punc
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BIGRAM_FREQS_FILE=${TRAINING_TEXT}.bigram_freqs
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UNIGRAM_FREQS_FILE=${TRAINING_TEXT}.unigram_freqs
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TRAIN_NGRAMS_FILE=${TRAINING_TEXT}.train_ngrams
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GENERATE_DAWGS=1
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}
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# Function initializes font config with a unique font cache dir.
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initialize_fontconfig() {
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export FONT_CONFIG_CACHE=$(mktemp -d --tmpdir font_tmp.XXXXXXXXXX)
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local sample_path=${FONT_CONFIG_CACHE}/sample_text.txt
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echo "Text" >${sample_path}
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run_command text2image --fonts_dir=${FONTS_DIR} \
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--font="${FONTS[0]}" --outputbase=${sample_path} --text=${sample_path} \
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--fontconfig_tmpdir=${FONT_CONFIG_CACHE}
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}
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# Helper function for phaseI_generate_image. Generates the image for a single
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# language/font combination in a way that can be run in parallel.
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generate_font_image() {
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local font="$1"
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tlog "Rendering using ${font}"
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local fontname=$(echo ${font} | tr ' ' '_' | sed 's/,//g')
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local outbase=${TRAINING_DIR}/${LANG_CODE}.${fontname}.exp${EXPOSURE}
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local common_args="--fontconfig_tmpdir=${FONT_CONFIG_CACHE}"
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common_args+=" --fonts_dir=${FONTS_DIR} --strip_unrenderable_words"
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common_args+=" --fontconfig_refresh_config_file=false --leading=${LEADING}"
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common_args+=" --char_spacing=${CHAR_SPACING} --exposure=${EXPOSURE}"
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common_args+=" --outputbase=${outbase}"
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# add --writing_mode=vertical-upright to common_args if the font is
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# specified to be rendered vertically.
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for vfont in "${VERTICAL_FONTS[@]}"; do
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if [[ "${font}" == "${vfont}" ]]; then
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common_args+=" --writing_mode=vertical-upright "
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break
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fi
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done
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run_command text2image ${common_args} --font="${font}" \
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--text=${TRAINING_TEXT} ${TEXT2IMAGE_EXTRA_ARGS}
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check_file_readable ${outbase}.box ${outbase}.tif
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if (( ${EXTRACT_FONT_PROPERTIES} )) &&
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[[ -r ${TRAIN_NGRAMS_FILE} ]]; then
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tlog "Extracting font properties of ${font}"
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run_command text2image ${common_args} --font="${font}" \
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--ligatures=false --text=${TRAIN_NGRAMS_FILE} \
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--only_extract_font_properties --ptsize=32
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check_file_readable ${outbase}.fontinfo
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fi
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}
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# Phase I : Generate (I)mages from training text for each font.
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phase_I_generate_image() {
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local par_factor=$1
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if [[ -z ${par_factor} || ${par_factor} -le 0 ]]; then
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par_factor=1
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fi
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tlog "\n=== Phase I: Generating training images ==="
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if [[ -z ${TRAINING_TEXT} ]] || [[ ! -r ${TRAINING_TEXT} ]]; then
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err_exit "Could not find training text file ${TRAINING_TEXT}"
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fi
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CHAR_SPACING="0.0"
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for EXPOSURE in $EXPOSURES; do
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if (( ${EXTRACT_FONT_PROPERTIES} )) && [[ -r ${BIGRAM_FREQS_FILE} ]]; then
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# Parse .bigram_freqs file and compose a .train_ngrams file with text
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# for tesseract to recognize during training. Take only the ngrams whose
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# combined weight accounts for 95% of all the bigrams in the language.
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NGRAM_FRAC=$(cat ${BIGRAM_FREQS_FILE} \
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| awk '{s=s+$2}; END {print (s/100)*p}' p=99)
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cat ${BIGRAM_FREQS_FILE} | sort -rnk2 \
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| awk '{s=s+$2; if (s <= x) {printf "%s ", $1; } }' \
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x=${NGRAM_FRAC} > ${TRAIN_NGRAMS_FILE}
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check_file_readable ${TRAIN_NGRAMS_FILE}
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fi
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local counter=0
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for font in "${FONTS[@]}"; do
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generate_font_image "${font}" &
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let counter=counter+1
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let rem=counter%par_factor
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if [[ "${rem}" -eq 0 ]]; then
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wait
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fi
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done
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wait
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# Check that each process was successful.
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for font in "${FONTS[@]}"; do
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local fontname=$(echo ${font} | tr ' ' '_' | sed 's/,//g')
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local outbase=${TRAINING_DIR}/${LANG_CODE}.${fontname}.exp${EXPOSURE}
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check_file_readable ${outbase}.box ${outbase}.tif
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done
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done
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}
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# Phase UP : Generate (U)nicharset and (P)roperties file.
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phase_UP_generate_unicharset() {
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tlog "\n=== Phase UP: Generating unicharset and unichar properties files ==="
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local box_files=$(ls ${TRAINING_DIR}/*.box)
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run_command unicharset_extractor -D "${TRAINING_DIR}/" ${box_files}
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local outfile=${TRAINING_DIR}/unicharset
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UNICHARSET_FILE="${TRAINING_DIR}/${LANG_CODE}.unicharset"
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check_file_readable ${outfile}
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mv ${outfile} ${UNICHARSET_FILE}
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XHEIGHTS_FILE="${TRAINING_DIR}/${LANG_CODE}.xheights"
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check_file_readable ${UNICHARSET_FILE}
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run_command set_unicharset_properties \
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-U ${UNICHARSET_FILE} -O ${UNICHARSET_FILE} -X ${XHEIGHTS_FILE} \
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--script_dir=${LANGDATA_ROOT}
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check_file_readable ${XHEIGHTS_FILE}
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}
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# Phase D : Generate (D)awg files from unicharset file and wordlist files
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phase_D_generate_dawg() {
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tlog "\n=== Phase D: Generating Dawg files ==="
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# Skip if requested
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if [[ ${GENERATE_DAWGS} -eq 0 ]]; then
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tlog "Skipping ${phase_name}"
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return
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fi
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# Output files
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WORD_DAWG=${TRAINING_DIR}/${LANG_CODE}.word-dawg
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FREQ_DAWG=${TRAINING_DIR}/${LANG_CODE}.freq-dawg
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PUNC_DAWG=${TRAINING_DIR}/${LANG_CODE}.punc-dawg
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NUMBER_DAWG=${TRAINING_DIR}/${LANG_CODE}.number-dawg
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BIGRAM_DAWG=${TRAINING_DIR}/${LANG_CODE}.bigram-dawg
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# Word DAWG
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local freq_wordlist_file=${TRAINING_DIR}/${LANG_CODE}.wordlist.clean.freq
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if [[ -s ${WORDLIST_FILE} ]]; then
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tlog "Generating word Dawg"
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check_file_readable ${UNICHARSET_FILE}
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run_command wordlist2dawg -r 1 ${WORDLIST_FILE} ${WORD_DAWG} \
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${UNICHARSET_FILE}
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check_file_readable ${WORD_DAWG}
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FREQ_DAWG_SIZE=100
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head -n ${FREQ_DAWG_SIZE} ${WORDLIST_FILE} > ${freq_wordlist_file}
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fi
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# Freq-word DAWG
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if [[ -s ${freq_wordlist_file} ]]; then
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check_file_readable ${UNICHARSET_FILE}
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tlog "Generating frequent-word Dawg"
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run_command wordlist2dawg -r 1 ${freq_wordlist_file} \
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${FREQ_DAWG} ${UNICHARSET_FILE}
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check_file_readable ${FREQ_DAWG}
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fi
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# Punctuation DAWG
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# -r arguments to wordlist2dawg denote RTL reverse policy
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# (see Trie::RTLReversePolicy enum in third_party/tesseract/dict/trie.h).
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# We specify 0/RRP_DO_NO_REVERSE when generating number DAWG,
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# 1/RRP_REVERSE_IF_HAS_RTL for freq and word DAWGS,
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# 2/RRP_FORCE_REVERSE for the punctuation DAWG.
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local punc_reverse_policy=0;
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case ${LANG_CODE} in
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ara | div| fas | pus | snd | syr | uig | urd | heb | yid )
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punc_reverse_policy=2 ;;
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* ) ;;
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esac
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if [[ ! -s ${PUNC_FILE} ]]; then
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PUNC_FILE="${LANGDATA_ROOT}/common.punc"
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fi
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check_file_readable ${PUNC_FILE}
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run_command wordlist2dawg -r ${punc_reverse_policy} \
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${PUNC_FILE} ${PUNC_DAWG} ${UNICHARSET_FILE}
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check_file_readable ${PUNC_DAWG}
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# Numbers DAWG
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if [[ -s ${NUMBERS_FILE} ]]; then
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run_command wordlist2dawg -r 0 \
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${NUMBERS_FILE} ${NUMBER_DAWG} ${UNICHARSET_FILE}
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check_file_readable ${NUMBER_DAWG}
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fi
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# Bigram dawg
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if [[ -s ${WORD_BIGRAMS_FILE} ]]; then
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run_command wordlist2dawg -r 1 \
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${WORD_BIGRAMS_FILE} ${BIGRAM_DAWG} ${UNICHARSET_FILE}
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check_file_readable ${BIGRAM_DAWG}
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fi
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}
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# Phase E : (E)xtract .tr feature files from .tif/.box files
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phase_E_extract_features() {
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local box_config=$1
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local par_factor=$2
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if [[ -z ${par_factor} || ${par_factor} -le 0 ]]; then
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par_factor=1
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fi
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tlog "\n=== Phase E: Extracting features ==="
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local img_files=""
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for exposure in ${EXPOSURES}; do
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img_files=${img_files}' '$(ls ${TRAINING_DIR}/*.exp${exposure}.tif)
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done
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# Use any available language-specific configs.
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local config=""
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if [[ -r ${LANGDATA_ROOT}/${LANG_CODE}/${LANG_CODE}.config ]]; then
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config=${LANGDATA_ROOT}/${LANG_CODE}/${LANG_CODE}.config
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fi
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OLD_TESSDATA_PREFIX=${TESSDATA_PREFIX}
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export TESSDATA_PREFIX=${TESSDATA_DIR}
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tlog "Using TESSDATA_PREFIX=${TESSDATA_PREFIX}"
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local counter=0
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for img_file in ${img_files}; do
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run_command tesseract ${img_file} ${img_file%.*} \
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${box_config} ${config} &
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let counter=counter+1
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let rem=counter%par_factor
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if [[ "${rem}" -eq 0 ]]; then
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wait
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fi
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done
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wait
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export TESSDATA_PREFIX=${OLD_TESSDATA_PREFIX}
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# Check that all the output files were produced.
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for img_file in ${img_files}; do
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check_file_readable ${img_file%.*}.tr
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done
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}
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# Phase C : (C)luster feature prototypes in .tr into normproto file (cnTraining)
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# phaseC_cluster_prototypes ${TRAINING_DIR}/${LANG_CODE}.normproto
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phase_C_cluster_prototypes() {
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tlog "\n=== Phase C: Clustering feature prototypes (cnTraining) ==="
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local out_normproto=$1
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run_command cntraining -D "${TRAINING_DIR}/" \
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$(ls ${TRAINING_DIR}/*.tr)
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check_file_readable ${TRAINING_DIR}/normproto
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mv ${TRAINING_DIR}/normproto ${out_normproto}
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}
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# Phase S : (S)hape clustering
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phase_S_cluster_shapes() {
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if (( ! ${RUN_SHAPE_CLUSTERING} )); then
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tlog "\n=== Shape Clustering disabled ==="
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return
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fi
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check_file_readable ${LANGDATA_ROOT}/font_properties
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local font_props="-F ${LANGDATA_ROOT}/font_properties"
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if [[ -r ${TRAINING_DIR}/${LANG_CODE}.xheights ]] &&\
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[[ -s ${TRAINING_DIR}/${LANG_CODE}.xheights ]]; then
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font_props=${font_props}" -X ${TRAINING_DIR}/${LANG_CODE}.xheights"
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fi
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run_command shapeclustering \
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-D "${TRAINING_DIR}/" \
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-U ${TRAINING_DIR}/${LANG_CODE}.unicharset \
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-O ${TRAINING_DIR}/${LANG_CODE}.mfunicharset \
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${font_props} \
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$(ls ${TRAINING_DIR}/*.tr)
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check_file_readable ${TRAINING_DIR}/shapetable \
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${TRAINING_DIR}/${LANG_CODE}.mfunicharset
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}
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# Phase M : Clustering microfeatures (mfTraining)
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phase_M_cluster_microfeatures() {
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tlog "\n=== Phase M : Clustering microfeatures (mfTraining) ==="
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check_file_readable ${LANGDATA_ROOT}/font_properties
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font_props="-F ${LANGDATA_ROOT}/font_properties"
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if [[ -r ${TRAINING_DIR}/${LANG_CODE}.xheights ]] && \
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[[ -s ${TRAINING_DIR}/${LANG_CODE}.xheights ]]; then
|
|
font_props=${font_props}" -X ${TRAINING_DIR}/${LANG_CODE}.xheights"
|
|
fi
|
|
|
|
run_command mftraining \
|
|
-D "${TRAINING_DIR}/" \
|
|
-U ${TRAINING_DIR}/${LANG_CODE}.unicharset \
|
|
-O ${TRAINING_DIR}/${LANG_CODE}.mfunicharset \
|
|
${font_props} \
|
|
$(ls ${TRAINING_DIR}/*.tr)
|
|
check_file_readable ${TRAINING_DIR}/inttemp ${TRAINING_DIR}/shapetable \
|
|
${TRAINING_DIR}/pffmtable ${TRAINING_DIR}/${LANG_CODE}.mfunicharset
|
|
mv ${TRAINING_DIR}/inttemp ${TRAINING_DIR}/${LANG_CODE}.inttemp
|
|
mv ${TRAINING_DIR}/shapetable ${TRAINING_DIR}/${LANG_CODE}.shapetable
|
|
mv ${TRAINING_DIR}/pffmtable ${TRAINING_DIR}/${LANG_CODE}.pffmtable
|
|
mv ${TRAINING_DIR}/${LANG_CODE}.mfunicharset ${TRAINING_DIR}/${LANG_CODE}.unicharset
|
|
}
|
|
|
|
phase_B_generate_ambiguities() {
|
|
tlog "\n=== Phase B : ambiguities training ==="
|
|
|
|
# Check for manually created ambiguities data.
|
|
if [[ -r ${LANGDATA_ROOT}/${LANG_CODE}/${LANG_CODE}.unicharambigs ]]; then
|
|
tlog "Found file ${LANGDATA_ROOT}/${LANG_CODE}/${LANG_CODE}.unicharambigs"
|
|
cp ${LANGDATA_ROOT}/${LANG_CODE}/${LANG_CODE}.unicharambigs \
|
|
${TRAINING_DIR}/${LANG_CODE}.unicharambigs
|
|
# Make it writable, as it may be read-only in the client.
|
|
chmod u+w ${TRAINING_DIR}/${LANG_CODE}.unicharambigs
|
|
return
|
|
else
|
|
tlog "No unicharambigs file found!"
|
|
fi
|
|
|
|
# TODO: Add support for generating ambiguities automatically.
|
|
}
|
|
|
|
|
|
make__traineddata() {
|
|
tlog "\n=== Making final traineddata file ==="
|
|
local lang_prefix=${LANGDATA_ROOT}/${LANG_CODE}/${LANG_CODE}
|
|
|
|
# Combine available files for this language from the langdata dir.
|
|
if [[ -r ${lang_prefix}.config ]]; then
|
|
tlog "Copying ${lang_prefix}.config to ${TRAINING_DIR}"
|
|
cp ${lang_prefix}.config ${TRAINING_DIR}
|
|
chmod u+w ${TRAINING_DIR}/${LANG_CODE}.config
|
|
fi
|
|
if [[ -r ${lang_prefix}.cube-unicharset ]]; then
|
|
tlog "Copying ${lang_prefix}.cube-unicharset to ${TRAINING_DIR}"
|
|
cp ${lang_prefix}.cube-unicharset ${TRAINING_DIR}
|
|
chmod u+w ${TRAINING_DIR}/${LANG_CODE}.cube-unicharset
|
|
fi
|
|
if [[ -r ${lang_prefix}.cube-word-dawg ]]; then
|
|
tlog "Copying ${lang_prefix}.cube-word-dawg to ${TRAINING_DIR}"
|
|
cp ${lang_prefix}.cube-word-dawg ${TRAINING_DIR}
|
|
chmod u+w ${TRAINING_DIR}/${LANG_CODE}.cube-word-dawg
|
|
fi
|
|
if [[ -r ${lang_prefix}.params-model ]]; then
|
|
tlog "Copying ${lang_prefix}.params-model to ${TRAINING_DIR}"
|
|
cp ${lang_prefix}.params-model ${TRAINING_DIR}
|
|
chmod u+w ${TRAINING_DIR}/${LANG_CODE}.params-model
|
|
fi
|
|
|
|
# Compose the traineddata file.
|
|
run_command combine_tessdata ${TRAINING_DIR}/${LANG_CODE}.
|
|
|
|
# Copy it to the output dir, overwriting only if allowed by the cmdline flag.
|
|
if [[ ! -d ${OUTPUT_DIR} ]]; then
|
|
tlog "Creating new directory ${OUTPUT_DIR}"
|
|
mkdir -p ${OUTPUT_DIR}
|
|
fi
|
|
local destfile=${OUTPUT_DIR}/${LANG_CODE}.traineddata;
|
|
if [[ -f ${destfile} ]] && (( ! ${OVERWRITE} )); then
|
|
err_exit "File ${destfile} exists and no --overwrite specified";
|
|
fi
|
|
tlog "Moving ${TRAINING_DIR}/${LANG_CODE}.traineddata to ${OUTPUT_DIR}"
|
|
cp -f ${TRAINING_DIR}/${LANG_CODE}.traineddata ${destfile}
|
|
}
|
|
|