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_interval 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 synopsis above) or replacing a layer options can be used
instead. Different options apply to different types of training. Read
the [training
documentation](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
defined by net_spec (type:int default:-1)
'--max_iterations '
If set, exit after this many iterations. A negative value is
interpreted 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
default:false)
'--convert_to_int '
Convert the recognition model to an integer model. (type:bool
default: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
default: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
default:)
'--eval_listfile '
File listing eval files in lstmf training format. (type:string
default:)
'--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/TrainingTesseract-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-present).
01/11/2023 LSTMTRAINING(1)
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