I modified the makefile for ocrd-train to do fine-tuning. It is pasted
below:
export
SHELL := /bin/bash
LOCAL := $(PWD)/usr
PATH := $(LOCAL)/bin:$(PATH)
HOME := /home/ubuntu
TESSDATA = $(HOME)/tessdata_best
LANGDATA = $(HOME)/langdata
# Name of the model to be built
MODEL_NAME = frk
# Name of the model to continue from
CONTINUE_FROM = frk
# Normalization Mode - see src/training/language_specific.sh for details
NORM_MODE = 2
# Tesseract model repo to use. Default: $(TESSDATA_REPO)
TESSDATA_REPO = _best
# Train directory
TRAIN := data/train
# BEGIN-EVAL makefile-parser --make-help Makefile
help:
@echo ""
@echo " Targets"
@echo ""
@echo " unicharset Create unicharset"
@echo " lists Create lists of lstmf filenames for training
and eval"
@echo " training Start training"
@echo " proto-model Build the proto model"
@echo " leptonica Build leptonica"
@echo " tesseract Build tesseract"
@echo " tesseract-langs Download tesseract-langs"
@echo " langdata Download langdata"
@echo " clean Clean all generated files"
@echo ""
@echo " Variables"
@echo ""
@echo " MODEL_NAME Name of the model to be built"
@echo " CORES No of cores to use for compiling
leptonica/tesseract"
@echo " LEPTONICA_VERSION Leptonica version. Default:
$(LEPTONICA_VERSION)"
@echo " TESSERACT_VERSION Tesseract commit. Default:
$(TESSERACT_VERSION)"
@echo " LANGDATA_VERSION Tesseract langdata version. Default:
$(LANGDATA_VERSION)"
@echo " TESSDATA_REPO Tesseract model repo to use. Default:
$(TESSDATA_REPO)"
@echo " TRAIN Train directory"
@echo " RATIO_TRAIN Ratio of train / eval training data"
# END-EVAL
# Ratio of train / eval training data
RATIO_TRAIN := 0.90
ALL_BOXES = data/all-boxes
ALL_LSTMF = data/all-lstmf
# Create unicharset
unicharset: data/unicharset
# Create lists of lstmf filenames for training and eval
lists: $(ALL_LSTMF) data/list.train data/list.eval
data/list.train: $(ALL_LSTMF)
total=`cat $(ALL_LSTMF) | wc -l` \
no=`echo "$$total * $(RATIO_TRAIN) / 1" | bc`; \
head -n "$$no" $(ALL_LSTMF) > "$@"
data/list.eval: $(ALL_LSTMF)
total=`cat $(ALL_LSTMF) | wc -l` \
no=`echo "($$total - $$total * $(RATIO_TRAIN)) / 1" | bc`; \
tail -n "+$$no" $(ALL_LSTMF) > "$@"
# Start training
training: data/$(MODEL_NAME).traineddata
data/unicharset: $(ALL_BOXES)
combine_tessdata -u $(TESSDATA)/$(CONTINUE_FROM).traineddata
$(TESSDATA)/$(CONTINUE_FROM).
unicharset_extractor --output_unicharset "$(TRAIN)/my.unicharset"
--norm_mode $(NORM_MODE) "$(ALL_BOXES)"
merge_unicharsets $(TESSDATA)/$(CONTINUE_FROM).lstm-unicharset
$(TRAIN)/my.unicharset "$@"
$(ALL_BOXES): $(sort $(patsubst %.tif,%.box,$(wildcard $(TRAIN)/*.tif)))
find $(TRAIN) -name '*.box' -exec cat {} \; > "$@"
$(TRAIN)/%.box: $(TRAIN)/%.tif $(TRAIN)/%-gt.txt
python generate_line_box.py -i "$(TRAIN)/$*.tif" -t "$(TRAIN)/$*-gt.txt" >
"$@"
$(ALL_LSTMF): $(sort $(patsubst %.tif,%.lstmf,$(wildcard $(TRAIN)/*.tif)))
find $(TRAIN) -name '*.lstmf' -exec echo {} \; | sort -R -o "$@"
$(TRAIN)/%.lstmf: $(TRAIN)/%.box
tesseract $(TRAIN)/$*.tif $(TRAIN)/$* --psm 6 lstm.train
# Build the proto model
proto-model: data/$(MODEL_NAME)/$(MODEL_NAME).traineddata
data/$(MODEL_NAME)/$(MODEL_NAME).traineddata: $(LANGDATA) data/unicharset
combine_lang_model \
--input_unicharset data/unicharset \
--script_dir $(LANGDATA) \
--words $(LANGDATA)/$(MODEL_NAME)/$(MODEL_NAME).wordlist \
--numbers $(LANGDATA)/$(MODEL_NAME)/$(MODEL_NAME).numbers \
--puncs $(LANGDATA)/$(MODEL_NAME)/$(MODEL_NAME).punc \
--output_dir data/ \
--lang $(MODEL_NAME)
data/checkpoints/$(MODEL_NAME)_checkpoint: unicharset lists proto-model
mkdir -p data/checkpoints
lstmtraining \
--continue_from $(TESSDATA)/$(CONTINUE_FROM).lstm \
--old_traineddata $(TESSDATA)/$(CONTINUE_FROM).traineddata \
--traineddata data/$(MODEL_NAME)/$(MODEL_NAME).traineddata \
--model_output data/checkpoints/$(MODEL_NAME) \
--debug_interval -1 \
--train_listfile data/list.train \
--eval_listfile data/list.eval \
--sequential_training \
--max_iterations 3000
data/$(MODEL_NAME).traineddata: data/checkpoints/$(MODEL_NAME)_checkpoint
lstmtraining \
--stop_training \
--continue_from $^ \
--old_traineddata $(TESSDATA)/$(CONTINUE_FROM).traineddata \
--traineddata data/$(MODEL_NAME)/$(MODEL_NAME).traineddata \
--model_output $@
# Clean all generated files
clean:
find data/train -name '*.box' -delete
find data/train -name '*.lstmf' -delete
rm -rf data/all-*
rm -rf data/list.*
rm -rf data/$(MODEL_NAME)
rm -rf data/unicharset
rm -rf data/checkpoints
On Fri, Jun 29, 2018 at 5:31 PM Lorenzo Bolzani <l.bolz...@gmail.com> wrote:
>
>
> Hi,
> I'm trying to do fine tuning of an existing model using line images and
> text labels. I'm running this version:
>
> tesseract 4.0.0-beta.3-56-g5fda
> leptonica-1.76.0
> libgif 5.1.4 : libjpeg 8d (libjpeg-turbo 1.4.2) : libpng 1.2.54 :
> libtiff 4.0.6 : zlib 1.2.8 : libwebp 0.4.4 : libopenjp2 2.3.0
> Found AVX2
> Found AVX
> Found SSE
>
>
>
> I used OCR-D to generate lstmf files for the demo data.
>
> If I run the make command it works fine.
>
> make training MODEL_NAME=prova
>
> Now I isolated this command from the build:
>
> lstmtraining \
> --traineddata data/prova/prova.traineddata \
> --net_spec "[1,36,0,1 Ct3,3,16 Mp3,3 Lfys48 Lfx96 Lrx96 Lfx256 O1c`head
> -n1 data/unicharset`]" \
> --model_output data/checkpoints/prova \
> --learning_rate 20e-4 \
> --train_listfile data/list.train \
> --eval_listfile data/list.eval \
> --max_iterations 10000
>
> and it works fine.
>
> Now I'm trying to modify it to fine tune the existing eng model. I made a
> few attempts, all ending into different errors (see the attached file for
> full output).
>
> I used:
>
> combine_tessdata -e /usr/local/share/tessdata/eng.traineddata
> extracted/eng.lstm
>
> to extract the eng.lstm model.
>
> This seems to works but I'm not sure it is the correct.
>
> lstmtraining \
> --continue_from extracted/eng.lstm \
> --traineddata data/prova/prova.traineddata \
> --old_traineddata extracted/eng.traineddata \
> --model_output data/checkpoints/prova \
> --learning_rate 20e-4 \
> --train_listfile data/list.train \
> --eval_listfile data/list.eval \
> --max_iterations 10000
>
> (extracted/eng.traineddata is just a copy of eng.traineddata)
>
>
> The training resume exactly with the RMS of prova_checkpoint (6%) so it
> looks like it is training from that checkpoint, not the eng.lstm.
>
> Is this correct? What should I change?
>
> I'm following this guide:
>
>
> https://github.com/tesseract-ocr/tesseract/wiki/TrainingTesseract-4.00#fine-tuning-for--a-few-characters
>
>
> I think continue_from and traineddata should refer to the eng model and
> old_traineddata should point to prova.traineddata, but if I do that I get a
> segmentation fault:
>
> [...]
> !int_mode_:Error:Assert failed:in file weightmatrix.cpp, line 244
> !int_mode_:Error:Assert failed:in file weightmatrix.cpp, line 244
> Segmentation fault
>
> What am I missing?
>
>
> Thanks, bye
>
> Lorenzo
>
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--
____________________________________________________________
भजन - कीर्तन - आरती @ http://bhajans.ramparivar.com
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