combine_lang_model1

Usage

Overview

combine_lang_model(1) generates a starter traineddata file that can be used to train an LSTM-based neural network model. It takes as input a unicharset and an optional set of wordlists. It eliminates the need to run set_unicharset_properties(1), wordlist2dawg(1), some non-existent binary to generate the recoder (unicode compressor), and finally combine_tessdata(1).

Options

--lang lang

The language to use. Tesseract uses 3-character ISO 639-2 language codes. (See LANGUAGES)

--script_dir PATH

Directory name for input script unicharsets. It should point to the location of langdata (github repo) directory. (type:string default:)

--input_unicharset FILE

Unicharset to complete and use in encoding. It can be a hand-created file with incomplete fields. Its basic and script properties will be set before it is used. (type:string default:)

--lang_is_rtl BOOL

True if language being processed is written right-to-left (eg Arabic/Hebrew). (type:bool default:false)

--pass_through_recoder BOOL

If true, the recoder is a simple pass-through of the unicharset. Otherwise, potentially a compression of it by encoding Hangul in Jamos, decomposing multi-unicode symbols into sequences of unicodes, and encoding Han using the data in the radical_table_data, which must be the content of the file: langdata/radical-stroke.txt. (type:bool default:false)

--version_str STRING

An arbitrary version label to add to traineddata file (type:string default:)

--words FILE

(Optional) File listing words to use for the system dictionary (type:string default:)

--numbers FILE

(Optional) File listing number patterns (type:string default:)

--puncs FILE

(Optional) File listing punctuation patterns. The words/puncs/numbers lists may be all empty. If any are non-empty then puncs must be non-empty. (type:string default:)

--output_dir PATH

Root directory for output files. Output files will be written to <output_dir>/<lang>/<lang>.* (type:string default:)

Resources

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

Copying

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