tesstrain | Train Tesseract LSTM with make | Computer Vision library
kandi X-RAY | tesstrain Summary
kandi X-RAY | tesstrain Summary
Training workflow for Tesseract 4 as a Makefile for dependency tracking and building the required software from source.
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Top functions reviewed by kandi - BETA
- Generate training images
- Check if files are readable
- Make font name
- Make the outbase output
- Parse command line arguments
- Log error message
- Build LSTM training data
- Run command
- Set language specific parameters
- Generate a font image
- Extract features
- Generate unicharset and uncharset properties
- Setup the logging console
- Setup logging
- Annotate the minimum value of the minimum iteration
- Clean up training data
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tesstrain Examples and Code Snippets
Community Discussions
Trending Discussions on tesstrain
QUESTION
I have a datasets with a lot of gt.txt and tiff files about 1000 files, I tried to use the tesstrain project and run the follow command make training MODEL_NAME=cmc7 TESSDATA=path/to/tessdata_best This command run with success but when I try to use the traineddata It doesn't work as expected. My question is what is the right form to training my datasets for tesseract? Thank you.
...ANSWER
Answered 2020-Aug-30 at 03:17To train my dataset with images I use 2 types of files in adition to the images:
- the gt.txt files with the expected output
- box files generated out of the images with the changes I want to train the model with
I place all 3 files inside tesstrain/data/my-model-ground-truth and run the following command from the tesstrain folder:
QUESTION
I would like to shuffle output of find
BUT with a fixed seed, so that every time I run the command I get the same output.
Here's how I shuffle:
...ANSWER
Answered 2020-Feb-17 at 17:32You can create your fixed_random function, using openssl to generate your random-source flow, like this
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install tesstrain
You can use tesstrain like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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