在 2017年10月24日星期二 UTC+8下午10:52:02，Ibr写道： > > Hi, > I have the latest version of Tesseract and leptonica 1.74.4, ran the > command > training/lstmtraining --model_output > /home/ibr/latest_leptonica_4/lstmf_old_jpn/jpn \ > --continue_from /home/ibr/latest_leptonica_4/jpn_tune/extracted/jpn.lstm > \ > --traineddata > /home/ibr/latest_leptonica_4/lstmf_jpn_lep4/jpn/jpn.traineddata \ > --old_traineddata > /home/ibr/latest_leptonica_4/jpn_tune/original_traineddata/jpn.traineddata \ > --train_listfile > /home/ibr/latest_leptonica_4/jpn_tune/jpn.training_files.txt \ > --max_iterations 18000 > > then the command : > > training/lstmtraining --stop_training \ > --continue_from /path/to/fine_tune/results/lang_checkpoint \ > --traineddata /path/to/starter_traineddata/lang.traineddata \ > --model_output /paht/to/new/tuned_lang.traineddata/lang.traineddata > to create the traineddata file, yet I found that the accuracy of the > official "best_traineddata" is better that what I got, I saw on this ocr > group and some comments of GitHub that too many iterations don't give you > best results, so I was wondering what is the optimal iterations to get the > best results? > > the first command above is from, Fine Tune for +_ few characters, what are > the commands for "Fine Tuning Impact"? I tried the command : > > training/lstmtraining --model_output ~/tesstutorial/impact_from_small/impact \ > --continue_from ~/tesstutorial/engoutput/base_checkpoint \ > --traineddata ~/tesstutorial/engtrain/eng/eng.traineddata \ > --train_listfile ~/tesstutorial/engeval/eng.training_files.txt \ > --max_iterations 1200 > > but what is the next commands? > > Thanks > > > I have the same problem，after each training model, the recognition > accuracy is not better than "best".training_text come form github's > langdata ,append my train_text,language is chi_sim. > and I dot kown the difference between "chi_sim" "and chi_sim_vert"
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