Getting text data from online stored image (image taken from Github)

Code is as below

text <- tesseract::ocr("http://jeroen.github.io/images/testocr.png", engine = eng)

and result is as below

#OUTPUT

cat(text)
#> This is a lot of 12 point text to test the
#> ocr code and see if it works on all types
#> of file format.
#> 
#> The quick brown dog jumped over the
#> lazy fox. The quick brown dog jumped
#> over the lazy fox. The quick brown dog
#> jumped over the lazy fox. The quick
#> brown dog jumped over the lazy fox.

Avaiable Languages for Data extraction

By default this package only show and includes english training data. But windows and mac users can install additional training languages data set using this code tesseract_download(). Aslo you can see the list of languages that your R Studio have installed already. By this command tesseract_info() we can see the list of language currently installed.

Code and result is as below

#OUTPUT

tesseract_info()
#> $datapath
#> [1] "C:\\Users\\Khan\\AppData\\Local\\tesseract4\\tesseract4\\tessdata/"
#> 
#> $available
#> [1] "eng" "nld" "osd"
#> 
#> $version
#> [1] "4.1.0"
#> 
#> $configs
#>  [1] "alto"             "ambigs.train"     "api_config"       "bigram"          
#>  [5] "box.train"        "box.train.stderr" "digits"           "get.images"      
#>  [9] "hocr"             "inter"            "kannada"          "linebox"         
#> [13] "logfile"          "lstm.train"       "lstmbox"          "lstmdebug"       
#> [17] "makebox"          "pdf"              "quiet"            "rebox"           
#> [21] "strokewidth"      "tsv"              "txt"              "unlv"            
#> [25] "wordstrbox"

Alternate or improve image quality with Magick

The athunticty of OCR Process depends on quality of image we select. By Magick we can improve image quality by croping the image where text exists, removing noise. There are many magical functions which magick R packge contains that can use for improve the quality of picture.

Some are here.

1. “For croping : image_trim() : cropsout whitespace in the margins”
2. “Noise removal: image_reducenoise() : for automate removalable of noise”
3. “For rotate : image_rotate() : makes the text in different directions.”
4. “Brigtness and contrast: image_contrast(): to inhance brithness if there is any issue .”
5. “Resizeing of image : image_resize() : for setting text size.”

Also if you want more information see improve quality in this there are important tips to improve the quality of your input image.

Getting Data from PDF files

We can also read data from PDF Files. Code below shows locally stored files and online files to extract data from PDF.

Getting data from online Stored PDF file. For online pdf file i am using an dummy pdf file.

Code is as below

pngfile <- pdftools::pdf_convert('https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf', dpi = 600)
#> Converting page 1 to dummy_1.png... done!
text <- tesseract::ocr(pngfile)

and result is as below

#OUTPUT 


cat(text)
#> Dummy PDF file

Getting data from Locally Stored PDF file.

Code is as below

pngfile <- pdftools::pdf_convert('C:/Users/Khan/Desktop/AT1/doc1.pdf', dpi = 600)
#> Converting page 1 to doc1_1.png... done!
text <- tesseract::ocr(pngfile)

and result is as below

#OUTPUT

cat(text)
#> 1.3 R and statistics
#> 
#> Our introduction to the R environment did not mention statistics, yet many people use R as a
#> statistics system. We prefer to think of it of an environment within which many classical and
#> modern statistical techniques have been implemented. A few of these are built into the base R
#> environment, but many are supplied as packages. There are about 25 packages supplied with
#> R (called “standard” and “recommended” packages) and many more are available through the
#> CRAN family of Internet sites (via https://CRAN.R-project.org) and elsewhere. More details
#> on packages are given later (see Chapter 13 [Packages], page 77).
#> 
#> Most classical statistics and much of the latest methodology is available for use with R, but
#> users may need to be prepared to do a little work to find it.
#> 
#> Chapter 1: Introduction and preliminaries 3
#> 
#> There is an important difference in philosophy between S (and hence R) and the other
#> 
#> main statistical systems. In S a statistical analysis is normally done as a series of steps, with
#> intermediate results being stored in objects. Thus whereas SAS and SPSS will give copious
#> output from a regression or discriminant analysis, R will give minimal output and store the
#> results in a fit object for subsequent interrogation by further R functions.
#> 
#> 1.4 R and the window system
#> 
#> The most convenient way to use R 1s at a graphics workstation running a windowing system.
#> This guide 1s aimed at users who have this facility. In particular we will occasionally refer to
#> the use of R on an X window system although the vast bulk of what 1s said applies generally to
#> any implementation of the R environment.
#> 
#> Most users will find it necessary to interact directly with the operating system on their
#> computer from time to time. In this guide, we mainly discuss interaction with the operating
#> system on UNIX machines. If you are running R under Windows or macOS you will need to
#> make some small adjustments.
#> 
#> Setting up a workstation to take full advantage of the customizable features of R is a straightforward
#> if somewhat tedious procedure, and will not be considered further here. Users in difficulty
#> should seek local expert help.
#> 
#> 1.5 Using R interactively
#> 
#> When you use the R program it issues a prompt when it expects input commands. The default
#> prompt is ‘>’, which on UNIX might be the same as the shell prompt, and so it may appear that
#> nothing is happening. However, as we shall see, it is easy to change to a different R prompt if
#> you wish. We will assume that the UNIX shell prompt is ‘$’.
#> 
#> In using R under UNIX the suggested procedure for the first occasion is as follows:
#> 
#> 1. Create a separate sub-directory, say work, to hold data files on which you will use R for
#> this problem. This will be the working directory whenever you use R for this particular
#> problem.

References:

Some helpful links which I use and for advance image processing things Also i use image of “R for Beginners” book for extracting data.

Alternate or improve quality of Image

Alternate or improve quality of Image

Getting data from image

OCR Engine and Package ‘tesseract’