lstmtraining - Man Page

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

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​.

'--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:)

lstmtraining(1) was first made available for tesseract4​.00​.00alpha​.

Main web site: https://github​.com/tesseract-ocr Information on training tesseract LSTM: https://tesseract-ocr​.github​.io/tessdoc/TrainingTesseract-4​.00​.html

tesseract(1)

Copyright (C) 2012 Google, Inc​. Licensed under the Apache License, Version 2​.0

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