Hi Ayush, psm 6 and 7 do some extra pre-processing of the image, 13 does much less.
Unless your image contains text like this: ---- ==== .... I would not expect much difference between PSM 6/7 and 13. While PSM 13 solves some problems I got more "ghost letters" errors (letters that are repeated more than once or split in similar variations, like O becoming O0). So this may not be an ideal solution. Also there is no reason why a clean single line of text should not work with 6 or 7. For some single line images with messy background I found that PSM 6 works better than 7. Lorenzo Il giorno ven 6 set 2019 alle ore 11:04 Ayush Pandey <xapianay...@gmail.com> ha scritto: > Hi Lorenzo. The empty output was due to the fact that I was using 7 as PSM > parameter. Using 13 as PSM parameter completely eliminated the problem. > > On Friday, September 6, 2019 at 12:34:22 PM UTC+5:30, Lorenzo Blz wrote: >> >> Can you please share an example? >> >> An empty output usually means that it failed to recognize the black parts >> as text, this could be because the text is too big or too small or a wrong >> dpi setting. Or the image is not reasonably clean. >> >> To better understand the problem you can try to downscale the images >> (according to some tests done by a user on this forum 35/50px is what >> worked best for him), try different dpi settings, remove borders, denoise, >> etc. Compare images that work with the ones who do not. >> >> >> >> Lorenzo >> >> >> >> >> >> >> Il giorno gio 5 set 2019 alle ore 10:48 Ayush Pandey <xapia...@gmail.com> >> ha scritto: >> >>> Hi shree, >>> Thank you so much for your response. I also wanted to ask, >>> I do get an empty output on a lot of images, after training, the height and >>> width of the image in pixels is usually > 100. Apart from changing the psm >>> value, is there any other way to reduce this. >>> >>> On Thursday, September 5, 2019 at 2:00:20 PM UTC+5:30, shree wrote: >>>> >>>> See >>>> https://github.com/tesseract-ocr/tesstrain/wiki/GT4HistOCR#tesseract-fails-to-create-lstm-files >>>> >>>> On Thu, Sep 5, 2019 at 1:25 PM Ayush Pandey <xapia...@gmail.com> wrote: >>>> >>>>> Tesseract Version: 4.1.0 >>>>> >>>>> I am trying to fine tune tesseract on custom dataset with the >>>>> following Makefile: >>>>> >>>>> export >>>>> >>>>> SHELL := /bin/bash >>>>> HOME := $(PWD) >>>>> TESSDATA = $(HOME)/tessdata >>>>> LANGDATA = $(HOME)/langdata >>>>> >>>>> # Train directory >>>>> # TRAIN := $(HOME)/train_data >>>>> TRAIN := /media/vimaan/Data/OCR/tesseract_train >>>>> >>>>> # Name of the model to be built >>>>> MODEL_NAME = eng >>>>> LANG_CODE = eng >>>>> >>>>> # Name of the model to continue from >>>>> CONTINUE_FROM = eng >>>>> >>>>> TESSDATA_REPO = _best >>>>> >>>>> # Normalization Mode - see src/training/language_specific.sh for details >>>>> NORM_MODE = 1 >>>>> >>>>> # 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 >>>>> lists: $(ALL_LSTMF) >>>>> >>>>> train-lists: 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) >>>>> mkdir -p data/$(START_MODEL) >>>>> 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 data/$(START_MODEL)/$(START_MODEL).lstm-unicharset >>>>> $(GROUND_TRUTH_DIR)/my.unicharset "$@" >>>>> 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)/$* --dpi 300 --psm 7 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 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 170000 >>>>> >>>>> 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 >>>>> >>>>> The number of .lstmf files being generated is significantly lower than >>>>> .box files being generated. >>>>> For eg: >>>>> Number of .tif files: 10k >>>>> Number of .gt.txt files: 10k >>>>> Number of .box files: 10k >>>>> Number of .lstmf files: 8k. >>>>> Could anyone point me out to the possible reasons for this issue >>>>> >>>>> On Friday, June 29, 2018 at 5:39:09 PM UTC+5:30, shree wrote: >>>>>> >>>>>> 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.bo...@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 >>>>>>> >>>>>>> -- >>>>>>> You received this message because you are subscribed to the Google >>>>>>> Groups "tesseract-ocr" group. >>>>>>> To unsubscribe from this group and stop receiving emails from it, >>>>>>> send an email to tesser...@googlegroups.com. >>>>>>> To post to this group, send email to tesser...@googlegroups.com. >>>>>>> Visit this group at https://groups.google.com/group/tesseract-ocr. >>>>>>> To view this discussion on the web visit >>>>>>> https://groups.google.com/d/msgid/tesseract-ocr/CAMgOLLyOJN31PdWQumXPO3JjuAc1Yz2BZYpMd4ftzBHgZkEaxA%40mail.gmail.com >>>>>>> <https://groups.google.com/d/msgid/tesseract-ocr/CAMgOLLyOJN31PdWQumXPO3JjuAc1Yz2BZYpMd4ftzBHgZkEaxA%40mail.gmail.com?utm_medium=email&utm_source=footer> >>>>>>> . >>>>>>> For more options, visit https://groups.google.com/d/optout. >>>>>>> >>>>>> >>>>>> >>>>>> -- >>>>>> >>>>>> ____________________________________________________________ >>>>>> भजन - कीर्तन - आरती @ http://bhajans.ramparivar.com >>>>>> >>>>> -- >>>>> You received this message because you are subscribed to the Google >>>>> Groups "tesseract-ocr" group. >>>>> To unsubscribe from this group and stop receiving emails from it, send >>>>> an email to tesser...@googlegroups.com. >>>>> To view this discussion on the web visit >>>>> https://groups.google.com/d/msgid/tesseract-ocr/e3ba3b90-a8c8-4085-bec5-cf918034ba2a%40googlegroups.com >>>>> <https://groups.google.com/d/msgid/tesseract-ocr/e3ba3b90-a8c8-4085-bec5-cf918034ba2a%40googlegroups.com?utm_medium=email&utm_source=footer> >>>>> . >>>>> >>>> >>>> >>>> -- >>>> >>>> ____________________________________________________________ >>>> भजन - कीर्तन - आरती @ http://bhajans.ramparivar.com >>>> >>> -- >>> You received this message because you are subscribed to the Google >>> Groups "tesseract-ocr" group. >>> To unsubscribe from this group and stop receiving emails from it, send >>> an email to tesser...@googlegroups.com. >>> To view this discussion on the web visit >>> https://groups.google.com/d/msgid/tesseract-ocr/91e85125-a9fc-450b-b434-391d2d4bd974%40googlegroups.com >>> <https://groups.google.com/d/msgid/tesseract-ocr/91e85125-a9fc-450b-b434-391d2d4bd974%40googlegroups.com?utm_medium=email&utm_source=footer> >>> . >>> >> -- > You received this message because you are subscribed to the Google Groups > "tesseract-ocr" group. > To unsubscribe from this group and stop receiving emails from it, send an > email to tesseract-ocr+unsubscr...@googlegroups.com. > To view this discussion on the web visit > https://groups.google.com/d/msgid/tesseract-ocr/3f97c86f-cc85-4ade-9aee-bfe67c43f066%40googlegroups.com > <https://groups.google.com/d/msgid/tesseract-ocr/3f97c86f-cc85-4ade-9aee-bfe67c43f066%40googlegroups.com?utm_medium=email&utm_source=footer> > . > -- You received this message because you are subscribed to the Google Groups "tesseract-ocr" group. 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