#!/bin/bash # (C) Copyright 2014, Google Inc. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # This script defines functions that are used by tesstrain.sh # For a detailed description of the phases, see # https://github.com/tesseract-ocr/tesseract/wiki/TrainingTesseract # # USAGE: source tesstrain_utils.sh if [ "$(uname)" == "Darwin" ];then FONTS_DIR="/Library/Fonts/" else FONTS_DIR="/usr/share/fonts/" fi OUTPUT_DIR="/tmp/tesstrain/tessdata" OVERWRITE=0 LINEDATA=0 RUN_SHAPE_CLUSTERING=0 EXTRACT_FONT_PROPERTIES=1 WORKSPACE_DIR=$(mktemp -d) # Logging helper functions. tlog() { echo -e $* 2>&1 1>&2 | tee -a ${LOG_FILE} } err_exit() { echo -e "ERROR: "$* 2>&1 1>&2 | tee -a ${LOG_FILE} exit 1 } # Helper function to run a command and append its output to a log. Aborts early # if the program file is not found. # Usage: run_command CMD ARG1 ARG2... run_command() { local cmd=$(which $1) if [[ -z ${cmd} ]]; then err_exit "$1 not found" fi shift tlog "[$(date)] ${cmd} $@" ${cmd} "$@" 2>&1 1>&2 | tee -a ${LOG_FILE} # check completion status if [[ $? -gt 0 ]]; then err_exit "Program $(basename ${cmd}) failed. Abort." fi } # Check if all the given files exist, or exit otherwise. # Used to check required input files and produced output files in each phase. # Usage: check_file_readable FILE1 FILE2... check_file_readable() { for file in $@; do if [[ ! -r ${file} ]]; then err_exit "${file} does not exist or is not readable" fi done } # Sets the named variable to given value. Aborts if the value is missing or # if it looks like a flag. # Usage: parse_value VAR_NAME VALUE parse_value() { local val="$2" if [[ -z $val ]]; then err_exit "Missing value for variable $1" exit fi if [[ ${val:0:2} == "--" ]]; then err_exit "Invalid value $val passed for variable $1" exit fi eval $1=\"$val\" } # Does simple command-line parsing and initialization. parse_flags() { local i=0 while test $i -lt ${#ARGV[@]}; do local j=$((i+1)) case ${ARGV[$i]} in --) break;; --fontlist) fn=0 FONTS="" while test $j -lt ${#ARGV[@]}; do test -z "${ARGV[$j]}" && break test $(echo ${ARGV[$j]} | cut -c -2) = "--" && break FONTS[$fn]="${ARGV[$j]}" fn=$((fn+1)) j=$((j+1)) done i=$((j-1)) ;; --exposures) exp="" while test $j -lt ${#ARGV[@]}; do test -z "${ARGV[$j]}" && break test $(echo ${ARGV[$j]} | cut -c -2) = "--" && break exp="$exp ${ARGV[$j]}" j=$((j+1)) done parse_value "EXPOSURES" "$exp" i=$((j-1)) ;; --fonts_dir) parse_value "FONTS_DIR" ${ARGV[$j]} i=$j ;; --lang) parse_value "LANG_CODE" ${ARGV[$j]} i=$j ;; --langdata_dir) parse_value "LANGDATA_ROOT" ${ARGV[$j]} i=$j ;; --output_dir) parse_value "OUTPUT_DIR" ${ARGV[$j]} i=$j ;; --overwrite) OVERWRITE=1 ;; --linedata_only) LINEDATA=1 ;; --extract_font_properties) EXTRACT_FONT_PROPERTIES=1 ;; --noextract_font_properties) EXTRACT_FONT_PROPERTIES=0 ;; --tessdata_dir) parse_value "TESSDATA_DIR" ${ARGV[$j]} i=$j ;; --training_text) parse_value "TRAINING_TEXT" "${ARGV[$j]}" i=$j ;; --wordlist) parse_value "WORDLIST_FILE" ${ARGV[$j]} i=$j ;; *) err_exit "Unrecognized argument ${ARGV[$i]}" ;; esac i=$((i+1)) done if [[ -z ${LANG_CODE} ]]; then err_exit "Need to specify a language --lang" fi if [[ -z ${LANGDATA_ROOT} ]]; then err_exit "Need to specify path to language files --langdata_dir" fi if [[ -z ${TESSDATA_DIR} ]]; then if [[ -z ${TESSDATA_PREFIX} ]]; then err_exit "Need to specify a --tessdata_dir or have a "\ "TESSDATA_PREFIX variable defined in your environment" else TESSDATA_DIR="${TESSDATA_PREFIX}" fi fi # Location where intermediate files will be created. TRAINING_DIR=${WORKSPACE_DIR}/${LANG_CODE} # Location of log file for the whole run. LOG_FILE=${TRAINING_DIR}/tesstrain.log # Take training text and wordlist from the langdata directory if not # specified in the command-line. if [[ -z ${TRAINING_TEXT} ]]; then TRAINING_TEXT=${LANGDATA_ROOT}/${LANG_CODE}/${LANG_CODE}.training_text fi if [[ -z ${WORDLIST_FILE} ]]; then WORDLIST_FILE=${LANGDATA_ROOT}/${LANG_CODE}/${LANG_CODE}.wordlist fi WORD_BIGRAMS_FILE=${LANGDATA_ROOT}/${LANG_CODE}/${LANG_CODE}.word.bigrams NUMBERS_FILE=${LANGDATA_ROOT}/${LANG_CODE}/${LANG_CODE}.numbers PUNC_FILE=${LANGDATA_ROOT}/${LANG_CODE}/${LANG_CODE}.punc BIGRAM_FREQS_FILE=${TRAINING_TEXT}.bigram_freqs UNIGRAM_FREQS_FILE=${TRAINING_TEXT}.unigram_freqs TRAIN_NGRAMS_FILE=${TRAINING_TEXT}.train_ngrams GENERATE_DAWGS=1 } # Function initializes font config with a unique font cache dir. initialize_fontconfig() { export FONT_CONFIG_CACHE=$(mktemp -d --tmpdir font_tmp.XXXXXXXXXX) local sample_path=${FONT_CONFIG_CACHE}/sample_text.txt echo "Text" >${sample_path} run_command text2image --fonts_dir=${FONTS_DIR} \ --font="${FONTS[0]}" --outputbase=${sample_path} --text=${sample_path} \ --fontconfig_tmpdir=${FONT_CONFIG_CACHE} } # Helper function for phaseI_generate_image. Generates the image for a single # language/font combination in a way that can be run in parallel. generate_font_image() { local font="$1" tlog "Rendering using ${font}" local fontname=$(echo ${font} | tr ' ' '_' | sed 's/,//g') local outbase=${TRAINING_DIR}/${LANG_CODE}.${fontname}.exp${EXPOSURE} local common_args="--fontconfig_tmpdir=${FONT_CONFIG_CACHE}" common_args+=" --fonts_dir=${FONTS_DIR} --strip_unrenderable_words" common_args+=" --leading=${LEADING}" common_args+=" --char_spacing=${CHAR_SPACING} --exposure=${EXPOSURE}" common_args+=" --outputbase=${outbase}" # add --writing_mode=vertical-upright to common_args if the font is # specified to be rendered vertically. for vfont in "${VERTICAL_FONTS[@]}"; do if [[ "${font}" == "${vfont}" ]]; then common_args+=" --writing_mode=vertical-upright " break fi done run_command text2image ${common_args} --font="${font}" \ --text=${TRAINING_TEXT} ${TEXT2IMAGE_EXTRA_ARGS} check_file_readable ${outbase}.box ${outbase}.tif if ((EXTRACT_FONT_PROPERTIES)) && [[ -r ${TRAIN_NGRAMS_FILE} ]]; then tlog "Extracting font properties of ${font}" run_command text2image ${common_args} --font="${font}" \ --ligatures=false --text=${TRAIN_NGRAMS_FILE} \ --only_extract_font_properties --ptsize=32 check_file_readable ${outbase}.fontinfo fi } # Phase I : Generate (I)mages from training text for each font. phase_I_generate_image() { local par_factor=$1 if [[ -z ${par_factor} || ${par_factor} -le 0 ]]; then par_factor=1 fi tlog "\n=== Phase I: Generating training images ===" if [[ -z ${TRAINING_TEXT} ]] || [[ ! -r ${TRAINING_TEXT} ]]; then err_exit "Could not find training text file ${TRAINING_TEXT}" fi CHAR_SPACING="0.0" for EXPOSURE in $EXPOSURES; do if ((EXTRACT_FONT_PROPERTIES)) && [[ -r ${BIGRAM_FREQS_FILE} ]]; then # Parse .bigram_freqs file and compose a .train_ngrams file with text # for tesseract to recognize during training. Take only the ngrams whose # combined weight accounts for 95% of all the bigrams in the language. NGRAM_FRAC=$(cat ${BIGRAM_FREQS_FILE} \ | awk '{s=s+$2}; END {print (s/100)*p}' p=99) cat ${BIGRAM_FREQS_FILE} | sort -rnk2 \ | awk '{s=s+$2; if (s <= x) {printf "%s ", $1; } }' \ x=${NGRAM_FRAC} > ${TRAIN_NGRAMS_FILE} check_file_readable ${TRAIN_NGRAMS_FILE} fi local counter=0 for font in "${FONTS[@]}"; do generate_font_image "${font}" & let counter=counter+1 let rem=counter%par_factor if [[ "${rem}" -eq 0 ]]; then wait fi done wait # Check that each process was successful. for font in "${FONTS[@]}"; do local fontname=$(echo ${font} | tr ' ' '_' | sed 's/,//g') local outbase=${TRAINING_DIR}/${LANG_CODE}.${fontname}.exp${EXPOSURE} check_file_readable ${outbase}.box ${outbase}.tif done done } # Phase UP : Generate (U)nicharset and (P)roperties file. phase_UP_generate_unicharset() { tlog "\n=== Phase UP: Generating unicharset and unichar properties files ===" local box_files=$(ls ${TRAINING_DIR}/*.box) run_command unicharset_extractor -D "${TRAINING_DIR}/" ${box_files} local outfile=${TRAINING_DIR}/unicharset UNICHARSET_FILE="${TRAINING_DIR}/${LANG_CODE}.unicharset" check_file_readable ${outfile} mv ${outfile} ${UNICHARSET_FILE} XHEIGHTS_FILE="${TRAINING_DIR}/${LANG_CODE}.xheights" check_file_readable ${UNICHARSET_FILE} run_command set_unicharset_properties \ -U ${UNICHARSET_FILE} -O ${UNICHARSET_FILE} -X ${XHEIGHTS_FILE} \ --script_dir=${LANGDATA_ROOT} check_file_readable ${XHEIGHTS_FILE} } # Phase D : Generate (D)awg files from unicharset file and wordlist files phase_D_generate_dawg() { tlog "\n=== Phase D: Generating Dawg files ===" # Skip if requested if [[ ${GENERATE_DAWGS} -eq 0 ]]; then tlog "Skipping ${phase_name}" return fi # Output files WORD_DAWG=${TRAINING_DIR}/${LANG_CODE}.word-dawg FREQ_DAWG=${TRAINING_DIR}/${LANG_CODE}.freq-dawg PUNC_DAWG=${TRAINING_DIR}/${LANG_CODE}.punc-dawg NUMBER_DAWG=${TRAINING_DIR}/${LANG_CODE}.number-dawg BIGRAM_DAWG=${TRAINING_DIR}/${LANG_CODE}.bigram-dawg # Word DAWG local freq_wordlist_file=${TRAINING_DIR}/${LANG_CODE}.wordlist.clean.freq if [[ -s ${WORDLIST_FILE} ]]; then tlog "Generating word Dawg" check_file_readable ${UNICHARSET_FILE} run_command wordlist2dawg -r 1 ${WORDLIST_FILE} ${WORD_DAWG} \ ${UNICHARSET_FILE} check_file_readable ${WORD_DAWG} FREQ_DAWG_SIZE=100 head -n ${FREQ_DAWG_SIZE} ${WORDLIST_FILE} > ${freq_wordlist_file} fi # Freq-word DAWG if [[ -s ${freq_wordlist_file} ]]; then check_file_readable ${UNICHARSET_FILE} tlog "Generating frequent-word Dawg" run_command wordlist2dawg -r 1 ${freq_wordlist_file} \ ${FREQ_DAWG} ${UNICHARSET_FILE} check_file_readable ${FREQ_DAWG} fi # Punctuation DAWG # -r arguments to wordlist2dawg denote RTL reverse policy # (see Trie::RTLReversePolicy enum in third_party/tesseract/dict/trie.h). # We specify 0/RRP_DO_NO_REVERSE when generating number DAWG, # 1/RRP_REVERSE_IF_HAS_RTL for freq and word DAWGS, # 2/RRP_FORCE_REVERSE for the punctuation DAWG. local punc_reverse_policy=0; case ${LANG_CODE} in ara | div| fas | pus | snd | syr | uig | urd | heb | yid ) punc_reverse_policy=2 ;; * ) ;; esac if [[ ! -s ${PUNC_FILE} ]]; then PUNC_FILE="${LANGDATA_ROOT}/common.punc" fi check_file_readable ${PUNC_FILE} run_command wordlist2dawg -r ${punc_reverse_policy} \ ${PUNC_FILE} ${PUNC_DAWG} ${UNICHARSET_FILE} check_file_readable ${PUNC_DAWG} # Numbers DAWG if [[ -s ${NUMBERS_FILE} ]]; then run_command wordlist2dawg -r 0 \ ${NUMBERS_FILE} ${NUMBER_DAWG} ${UNICHARSET_FILE} check_file_readable ${NUMBER_DAWG} fi # Bigram dawg if [[ -s ${WORD_BIGRAMS_FILE} ]]; then run_command wordlist2dawg -r 1 \ ${WORD_BIGRAMS_FILE} ${BIGRAM_DAWG} ${UNICHARSET_FILE} check_file_readable ${BIGRAM_DAWG} fi } # Phase E : (E)xtract .tr feature files from .tif/.box files phase_E_extract_features() { local box_config=$1 local par_factor=$2 local ext=$3 if [[ -z ${par_factor} || ${par_factor} -le 0 ]]; then par_factor=1 fi tlog "\n=== Phase E: Generating ${ext} files ===" local img_files="" for exposure in ${EXPOSURES}; do img_files=${img_files}' '$(ls ${TRAINING_DIR}/*.exp${exposure}.tif) done # Use any available language-specific configs. local config="" if [[ -r ${LANGDATA_ROOT}/${LANG_CODE}/${LANG_CODE}.config ]]; then config=${LANGDATA_ROOT}/${LANG_CODE}/${LANG_CODE}.config fi OLD_TESSDATA_PREFIX=${TESSDATA_PREFIX} export TESSDATA_PREFIX=${TESSDATA_DIR} tlog "Using TESSDATA_PREFIX=${TESSDATA_PREFIX}" local counter=0 for img_file in ${img_files}; do run_command tesseract ${img_file} ${img_file%.*} \ ${box_config} ${config} & let counter=counter+1 let rem=counter%par_factor if [[ "${rem}" -eq 0 ]]; then wait fi done wait export TESSDATA_PREFIX=${OLD_TESSDATA_PREFIX} # Check that all the output files were produced. for img_file in ${img_files}; do check_file_readable "${img_file%.*}.${ext}" done } # Phase C : (C)luster feature prototypes in .tr into normproto file (cnTraining) # phaseC_cluster_prototypes ${TRAINING_DIR}/${LANG_CODE}.normproto phase_C_cluster_prototypes() { tlog "\n=== Phase C: Clustering feature prototypes (cnTraining) ===" local out_normproto=$1 run_command cntraining -D "${TRAINING_DIR}/" \ $(ls ${TRAINING_DIR}/*.tr) check_file_readable ${TRAINING_DIR}/normproto mv ${TRAINING_DIR}/normproto ${out_normproto} } # Phase S : (S)hape clustering phase_S_cluster_shapes() { if ((! RUN_SHAPE_CLUSTERING)); then tlog "\n=== Shape Clustering disabled ===" return fi check_file_readable ${LANGDATA_ROOT}/font_properties local font_props="-F ${LANGDATA_ROOT}/font_properties" if [[ -r ${TRAINING_DIR}/${LANG_CODE}.xheights ]] &&\ [[ -s ${TRAINING_DIR}/${LANG_CODE}.xheights ]]; then font_props=${font_props}" -X ${TRAINING_DIR}/${LANG_CODE}.xheights" fi run_command shapeclustering \ -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}/shapetable \ ${TRAINING_DIR}/${LANG_CODE}.mfunicharset } # Phase M : Clustering microfeatures (mfTraining) phase_M_cluster_microfeatures() { tlog "\n=== Phase M : Clustering microfeatures (mfTraining) ===" check_file_readable ${LANGDATA_ROOT}/font_properties font_props="-F ${LANGDATA_ROOT}/font_properties" if [[ -r ${TRAINING_DIR}/${LANG_CODE}.xheights ]] && \ [[ -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__lstmdata() { tlog "\n=== Constructing LSTM training data ===" local lang_prefix=${LANGDATA_ROOT}/${LANG_CODE}/${LANG_CODE} if [[ ! -d ${OUTPUT_DIR} ]]; then tlog "Creating new directory ${OUTPUT_DIR}" mkdir -p ${OUTPUT_DIR} fi # Copy available files for this language from the langdata dir. if [[ -r ${lang_prefix}.config ]]; then tlog "Copying ${lang_prefix}.config to ${OUTPUT_DIR}" cp ${lang_prefix}.config ${OUTPUT_DIR} chmod u+w ${OUTPUT_DIR}/${LANG_CODE}.config fi if [[ -r "${TRAINING_DIR}/${LANG_CODE}.unicharset" ]]; then tlog "Moving ${TRAINING_DIR}/${LANG_CODE}.unicharset to ${OUTPUT_DIR}" mv "${TRAINING_DIR}/${LANG_CODE}.unicharset" "${OUTPUT_DIR}" fi for ext in number-dawg punc-dawg word-dawg; do local src="${TRAINING_DIR}/${LANG_CODE}.${ext}" if [[ -r "${src}" ]]; then dest="${OUTPUT_DIR}/${LANG_CODE}.lstm-${ext}" tlog "Moving ${src} to ${dest}" mv "${src}" "${dest}" fi done for f in "${TRAINING_DIR}/${LANG_CODE}".*.lstmf; do tlog "Moving ${f} to ${OUTPUT_DIR}" mv "${f}" "${OUTPUT_DIR}" done local lstm_list="${OUTPUT_DIR}/${LANG_CODE}.training_files.txt" ls -1 "${OUTPUT_DIR}"/*.lstmf > "${lstm_list}" } make__traineddata() { tlog "\n=== Making final traineddata file ===" local lang_prefix=${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} }