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LSTMTRAINING(1) 						 LSTMTRAINING(1)

NAME
     lstmtraining - Training program for LSTM-based networks.

SYNOPSIS
     lstmtraining    --continue_from	train_output_dir/continue_from_lang.lstm
     --old_traineddata bestdata_dir/continue_from_lang.traineddata --traineddata
     train_output_dir/lang/lang.traineddata --max_iterations NNN  --debug_inter-
     val    0|-1    --train_listfile	train_output_dir/lang.training_files.txt
     --model_output train_output_dir/newlstmmodel

DESCRIPTION
     lstmtraining(1) trains LSTM-based networks using a list of lstmf files  and
     starter  traineddata  file  as the main input. Training from scratch is not
     recommended to be done by users. Finetuning (example command shown in  syn-
     opsis  above)  or	replacing a layer options can be used instead. Different
     options apply to different types of training. Read the [training documenta-
     tion](https://tesseract-ocr.github.io/tessdoc/TrainingTesseract-4.00.html)
     for details.

OPTIONS
     '--debug_interval '
	 How often to display the alignment. (type:int default:0)

     '--net_mode '
	 Controls network behavior. (type:int default:192)

     '--perfect_sample_delay '
	 How many imperfect samples between perfect ones. (type:int default:0)

     '--max_image_MB '
	 Max memory to use for images. (type:int default:6000)

     '--append_index '
	 Index in continue_from Network at which to attach the new  network  de-
	 fined by net_spec (type:int default:-1)

     '--max_iterations '
	 If  set,  exit  after	this many iterations. A negative value is inter-
	 preted as epochs, 0 means infinite iterations. (type:int default:0)

     '--target_error_rate '
	 Final error rate in percent. (type:double default:0.01)

     '--weight_range '
	 Range of initial random weights. (type:double default:0.1)

     '--learning_rate '
	 Weight factor for new deltas. (type:double default:0.001)

     '--momentum '
	 Decay factor for repeating deltas. (type:double default:0.5)

     '--adam_beta '
	 Decay factor for repeating deltas. (type:double default:0.999)

     '--stop_training '
	 Just convert the training model to  a	runtime  model.  (type:bool  de-
	 fault:false)

     '--convert_to_int '
	 Convert  the  recognition  model  to  an  integer model. (type:bool de-
	 fault:false)

     '--sequential_training '
	 Use the training files sequentially instead of round-robin.  (type:bool
	 default:false)

     '--debug_network '
	 Get info on distribution of weight values (type:bool default:false)

     '--randomly_rotate '
	 Train OSD and randomly turn training samples upside-down (type:bool de-
	 fault:false)

     '--net_spec '
	 Network specification (type:string default:)

     '--continue_from '
	 Existing model to extend (type:string default:)

     '--model_output '
	 Basename for output models (type:string default:lstmtrain)

     '--train_listfile '
	 File  listing training files in lstmf training format. (type:string de-
	 fault:)

     '--eval_listfile '
	 File listing eval files in  lstmf  training  format.  (type:string  de-
	 fault:)

     '--traineddata '
	 Starter traineddata with combined Dawgs/Unicharset/Recoder for language
	 model (type:string default:)

     '--old_traineddata '
	 When  changing  the  character set, this specifies the traineddata with
	 the old character set that is to be replaced (type:string default:)

HISTORY
     lstmtraining(1) was first made available for tesseract4.00.00alpha.

RESOURCES
     Main web site:  https://github.com/tesseract-ocr  Information  on	training
     tesseract	 LSTM:	 https://tesseract-ocr.github.io/tessdoc/TrainingTesser-
     act-4.00.html

SEE ALSO
     tesseract(1)

COPYING
     Copyright (C) 2012 Google, Inc. Licensed under the Apache License,  Version
     2.0

AUTHOR
     The  Tesseract  OCR engine was written by Ray Smith and his research groups
     at Hewlett Packard (1985-1995) and Google (2006-2018).

				   08/27/2026			 LSTMTRAINING(1)
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