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ocr-text-extraction | simple program | Computer Vision library

 by   jasonlfunk Python Version: Current License: MIT

 by   jasonlfunk Python Version: Current License: MIT

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kandi X-RAY | ocr-text-extraction Summary

ocr-text-extraction is a Python library typically used in Artificial Intelligence, Computer Vision applications. ocr-text-extraction has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However ocr-text-extraction build file is not available. You can download it from GitHub.
I am not actively supporting this script. It was just an experiment. Processes an image to extract the text portions. Primarily used for pre-processing for performing OCR. Implemented in Python using OpenCV. Based on the paper "Font and Background Color Independent Text Binarization" by T Kasar, J Kumar and A G Ramakrishnan http://www.m.cs.osakafu-u.ac.jp/cbdar2007/proceedings/papers/O1-1.pdf. Copyright (c) 2012, Jason Funk <jasonlfunk@gmail.com>.
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse

kandi-support Support

  • ocr-text-extraction has a low active ecosystem.
  • It has 200 star(s) with 194 fork(s). There are 15 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 5 open issues and 3 have been closed. On average issues are closed in 262 days. There are 5 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of ocr-text-extraction is current.
ocr-text-extraction Support
Best in #Computer Vision
Average in #Computer Vision
ocr-text-extraction Support
Best in #Computer Vision
Average in #Computer Vision

quality kandi Quality

  • ocr-text-extraction has 0 bugs and 0 code smells.
ocr-text-extraction Quality
Best in #Computer Vision
Average in #Computer Vision
ocr-text-extraction Quality
Best in #Computer Vision
Average in #Computer Vision

securitySecurity

  • ocr-text-extraction has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • ocr-text-extraction code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
ocr-text-extraction Security
Best in #Computer Vision
Average in #Computer Vision
ocr-text-extraction Security
Best in #Computer Vision
Average in #Computer Vision

license License

  • ocr-text-extraction is licensed under the MIT License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
ocr-text-extraction License
Best in #Computer Vision
Average in #Computer Vision
ocr-text-extraction License
Best in #Computer Vision
Average in #Computer Vision

buildReuse

  • ocr-text-extraction releases are not available. You will need to build from source code and install.
  • ocr-text-extraction has no build file. You will be need to create the build yourself to build the component from source.
ocr-text-extraction Reuse
Best in #Computer Vision
Average in #Computer Vision
ocr-text-extraction Reuse
Best in #Computer Vision
Average in #Computer Vision
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ocr-text-extraction Key Features

A simple program to extract the text from an image before performing OCR

ocr-text-extraction Examples and Code Snippets

No Code Snippets are available at this moment for ocr-text-extraction.Refer to component home page for details.

No Code Snippets are available at this moment for ocr-text-extraction.Refer to component home page for details.

Community Discussions

Trending Discussions on Computer Vision
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QUESTION

Image similarity in swift

Asked 2022-Mar-25 at 11:42

The swift vision similarity feature is able to assign a number to the variance between 2 images. Where 0 variance between the images, means the images are the same. As the number increases this that there is more and more variance between the images.

What I am trying to do is turn this into a percentage of similarity. So one image is for example 80% similar to the other image. Any ideas how I could arrange the logic to accomplish this:

import UIKit
import Vision
func featureprintObservationForImage(atURL url: URL) -> VNFeaturePrintObservation? {
let requestHandler = VNImageRequestHandler(url: url, options: [:])
let request = VNGenerateImageFeaturePrintRequest()
do {
  try requestHandler.perform([request])
  return request.results?.first as? VNFeaturePrintObservation
} catch {
  print("Vision error: \(error)")
  return nil
}
  }
 let apple1 = featureprintObservationForImage(atURL: Bundle.main.url(forResource:"apple1", withExtension: "jpg")!)
let apple2 = featureprintObservationForImage(atURL: Bundle.main.url(forResource:"apple2", withExtension: "jpg")!)
let pear = featureprintObservationForImage(atURL: Bundle.main.url(forResource:"pear", withExtension: "jpg")!)
var distance = Float(0)
try apple1!.computeDistance(&distance, to: apple2!)
var distance2 = Float(0)
try apple1!.computeDistance(&distance2, to: pear!)

ANSWER

Answered 2022-Mar-25 at 10:26

It depends on how you want to scale it. If you just want the percentage you could just use Float.greatestFiniteMagnitude as the maximum value.

1-(distance/Float.greatestFiniteMagnitude)*100

A better solution would probably be to set a lower ceiling and everything above that ceiling would just be 0% similarity.

1-(min(distance, 10)/10)*100

Here the artificial ceiling would be 10, but it can be any arbitrary number.

Source https://stackoverflow.com/questions/71615277

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

Vulnerabilities

No vulnerabilities reported

Install ocr-text-extraction

You can download it from GitHub.
You can use ocr-text-extraction 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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