display_ocr | Real-time image preprocess and OCR | Computer Vision library

 by   arturaugusto Python Version: Current License: GPL-2.0

kandi X-RAY | display_ocr Summary

kandi X-RAY | display_ocr Summary

display_ocr is a Python library typically used in Artificial Intelligence, Computer Vision, Deep Learning, Pytorch applications. display_ocr has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. However display_ocr build file is not available. You can download it from GitHub.

OpenCV-Python + python-tesseract real-time image preprocess and OCR. Trained data for 7 segments font avaliable under letsgodigital folder. Web-app using trained data: (Not always up, due to the low cost server). Tips to achieve better results: - Use erode to avoid gaps between the segments. - Avoid direct light on the display (I use parchment paper to diffuse the light).

            kandi-support Support

              display_ocr has a low active ecosystem.
              It has 200 star(s) with 61 fork(s). There are 15 watchers for this library.
              It had no major release in the last 6 months.
              There are 5 open issues and 3 have been closed. On average issues are closed in 69 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of display_ocr is current.

            kandi-Quality Quality

              display_ocr has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              display_ocr is licensed under the GPL-2.0 License. This license is Strong Copyleft.
              Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

            kandi-Reuse Reuse

              display_ocr releases are not available. You will need to build from source code and install.
              display_ocr has no build file. You will be need to create the build yourself to build the component from source.
              display_ocr saves you 153 person hours of effort in developing the same functionality from scratch.
              It has 382 lines of code, 22 functions and 4 files.
              It has low code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed display_ocr and discovered the below as its top functions. This is intended to give you an instant insight into display_ocr implemented functionality, and help decide if they suit your requirements.
            • This function is called by the main thread
            • Check if the given rectangle is a rectangle
            • Handle a shape event
            • Define a rectangle
            Get all kandi verified functions for this library.

            display_ocr Key Features

            No Key Features are available at this moment for display_ocr.

            display_ocr Examples and Code Snippets

            No Code Snippets are available at this moment for display_ocr.

            Community Discussions


            Performing OCR of Seven Segment Display images
            Asked 2021-Apr-19 at 06:01

            I'm working on performing OCR of energy meter displays: example 1 example 2 example 3

            I tried to use tesseract-ocr with the letsgodigital trained data. But the performance is very poor.

            I'm fairly new to the topic and this is what I've done:



            Answered 2021-Apr-19 at 06:01

            Notice how your power meters either use blue or green LEDs to light up the display; I suggest you use this color display to your advantage. What I'd do is select only one RGB channel based on the LED color. Then I can threshold it based on some algorithm or assumption. After that, you can do the downstream steps of cropping / resizing / transformation / OCR etc.

            For example, using your example image 1, look at its histogram here. Notice how there is a small peak of green to the right of the 150 mark.

            I take advantage of this, and set anything below 150 to zero. My assumption being that the green peak is the bright green LED in the image.

            Source https://stackoverflow.com/questions/67146380


            Issue to train tesseract-OCR 4 - Empy shape table
            Asked 2020-Dec-09 at 21:47

            I am trying to train Tesseract 4 with particular pictures (to read multimeters with 7 segments),

            please note that I am aware of the allready trained data from Arthur Augusto at https://github.com/arturaugusto/display_ocr but I need to train Tesseract over my own data.

            In order to train tess, I followed differents tutorials (as https://robipritrznik.medium.com/recognizing-vehicle-license-plates-on-images-using-tesseract-4-ocr-with-custom-trained-models-4ba9861595e7 or https://pretius.com/how-to-prepare-training-files-for-tesseract-ocr-and-improve-characters-recognition/)

            but i allways get problem when running the shapeclustering command with my own data

            (With example data as https://github.com/tesseract-ocr/tesseract/issues/1174#issuecomment-338448972, every things is working fine)

            Indeed when I try to do the shapeclusturing command it have this output screenshot Then my shape_table is empty and the trainig could'nt be efficient...

            With example data it's working fine and the shape_table is well filled

            I am guessing that I have issue with box file generation, here is my process to create box file :

            I use the



            Answered 2020-Dec-09 at 21:47

            Ok so finally I achieved to train tesseract.

            The solution is to add a --psm parameter when using the command

            Source https://stackoverflow.com/questions/65116781

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


            No vulnerabilities reported

            Install display_ocr

            You can download it from GitHub.
            You can use display_ocr 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.


            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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