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lstmeval(1)                                                        lstmeval(1)


NAME

       lstmeval - Evaluation program for LSTM-based networks.


SYNOPSIS

       lstmeval --model
       lang.lstm|modelname_checkpoint|modelname_N.NN_NN_NN.checkpoint
       [--traineddata lang/lang.traineddata] --eval_listfile
       lang.eval_files.txt [--verbosity N] [--max_image_MB NNNN]


DESCRIPTION

       lstmeval(1) evaluates LSTM-based networks. Either a recognition model
       or a training checkpoint can be given as input for evaluation along
       with a list of lstmf files. If evaluating a training checkpoint,
       --traineddata should also be specified. Intermediate training
       checkpoints can also be used.


OPTIONS

       --model FILE
           Name of model file (training or recognition) (type:string default:)

       --traineddata FILE
           If model is a training checkpoint, then traineddata must be the
           traineddata file that was given to the trainer (type:string
           default:)

       --eval_listfile FILE
           File listing sample files in lstmf training format. (type:string
           default:)

       --max_image_MB INT
           Max memory to use for images. (type:int default:2000)

       --verbosity INT
           Amount of diagnosting information to output (0-2). (type:int
           default:1)


HISTORY

       lstmeval(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 (2006-2018).

                                  08/31/2024                       lstmeval(1)

tesseract 5.4.1 - Generated Thu Oct 3 16:27:19 CDT 2024
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