tesstrain | Train Tesseract LSTM with make | Computer Vision library

 by   tesseract-ocr Python Version: 1.0.0 License: Apache-2.0

kandi X-RAY | tesstrain Summary

kandi X-RAY | tesstrain Summary

tesstrain is a Python library typically used in Artificial Intelligence, Computer Vision applications. tesstrain has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

Training workflow for Tesseract 4 as a Makefile for dependency tracking and building the required software from source.
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            kandi-support Support

              tesstrain has a low active ecosystem.
              It has 423 star(s) with 146 fork(s). There are 23 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 27 open issues and 209 have been closed. On average issues are closed in 107 days. There are 3 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of tesstrain is 1.0.0

            kandi-Quality Quality

              tesstrain has 0 bugs and 83 code smells.

            kandi-Security Security

              tesstrain has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              tesstrain code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              tesstrain is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              tesstrain releases are not available. You will need to build from source code and install.
              Build file is available. You can build the component from source.
              Installation instructions are not available. Examples and code snippets are available.
              It has 1994 lines of code, 24 functions and 11 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed tesstrain and discovered the below as its top functions. This is intended to give you an instant insight into tesstrain implemented functionality, and help decide if they suit your requirements.
            • 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
            Get all kandi verified functions for this library.

            tesstrain Key Features

            No Key Features are available at this moment for tesstrain.

            tesstrain Examples and Code Snippets

            No Code Snippets are available at this moment for tesstrain.

            Community Discussions

            QUESTION

            Tesseract how could I training my dataset
            Asked 2020-Aug-30 at 03:17

            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 03:17

            To 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:

            Source https://stackoverflow.com/questions/63640134

            QUESTION

            Shuffle output of find with fixed seed
            Asked 2020-Feb-17 at 18:54

            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 17:32

            You can create your fixed_random function, using openssl to generate your random-source flow, like this

            Source https://stackoverflow.com/questions/60266215

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install tesstrain

            You can download it from GitHub.
            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.

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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          • HTTPS

            https://github.com/tesseract-ocr/tesstrain.git

          • CLI

            gh repo clone tesseract-ocr/tesstrain

          • sshUrl

            git@github.com:tesseract-ocr/tesstrain.git

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