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javatesseract

Tesseract OCR does not work properly, how to be more accurate?

i am trying to recognize text of some screenshots with tess4j, but the output has many letters wrong or missing words. Here the code:

package duote;

import java.awt.Graphics2D;
import java.awt.Image;
import java.awt.Rectangle;
import java.awt.image.BufferedImage;
import java.io.File;
import java.io.IOException;

import javax.imageio.ImageIO;

import net.sourceforge.tess4j.ITessAPI;
import net.sourceforge.tess4j.ITesseract;
import net.sourceforge.tess4j.Tesseract;
import net.sourceforge.tess4j.Word;

public class Test
{
    public static void main(String[] args) throws IOException
    {
        String imageUrl = "C:/Users/alexc/eclipse-workspace/duote/src/images/apple.png";
        File imageFile = new File(imageUrl);
        Image image = ImageIO.read(imageFile);
        BufferedImage bi = toBufferedImage(image);
        ITesseract instance = new Tesseract();

        for(Word word : instance.getWords(bi, ITessAPI.TessPageIteratorLevel.RIL_WORD))
        {
          Rectangle rect = word.getBoundingBox();

          System.out.println(rect.getMinX()+","+rect.getMaxX()+","+rect.getMinY()+","+rect.getMaxY()
                            +": "+word.getText());
        }
    }

    public static BufferedImage toBufferedImage(Image img)
    {
      if (img instanceof BufferedImage)
      {
          return (BufferedImage) img;
      }

      // Create a buffered image with transparency
      BufferedImage bimage = new BufferedImage(img.getWidth(null), img.getHeight(null), BufferedImage.TYPE_INT_ARGB);

      // Draw the image on to the buffered image
      Graphics2D bGr = bimage.createGraphics();
      bGr.drawImage(img, 0, 0, null);
      bGr.dispose();

      // Return the buffered image
      return bimage;
    }
}

and this is the screenshot: some wrong letters

i tried with other screenshots and sometimes words are missing. Is there a way to be more accurate or am i wrong at something? thanks.

output:

185.0,200.0,8.0,26.0: 6
277.0,303.0,14.0,24.0: Mac
379.0,406.0,13.0,24.0: iPad
482.0,525.0,13.0,24.0: iPhcne
602.0,642.0,14.0,24.0: Watch
718.0,735.0,14.0,24.0: TV
812.0,849.0,14.0,24.0: Mus":
926.0,977.0,14.0,27.0: Supparl
1053.0,1071.0,10.0,27.0: Q
1147.0,1164.0,7.0,27.0: C]
514.0,523.0,96.0,104.0: .
514.0,681.0,97.0,138.0: Phone
694.0,738.0,97.0,137.0: 11
758.0,837.0,97.0,138.0: Pro
426.0,464.0,159.0,180.0: Pro
470.0,577.0,164.0,180.0: cameras.
584.0,622.0,159.0,180.0: Pro
628.0,716.0,159.0,185.0: display.
722.0,761.0,159.0,180.0: Pro
767.0,923.0,159.0,184.0: performance.
521.0,559.0,198.0,210.0: From
564.0,652.0,197.0,213.0: £30.99/m0.
658.0,673.0,201.0,210.0: or
678.0,718.0,198.0,210.0: £759
723.0,754.0,197.0,210.0: with
759.0,829.0,197.0,210.0: lrade-in.‘
578.0,691.0,236.0,252.0: Leammore)
724.0,771.0,236.0,255.0: Buy)
716.0,728.0,278.0,294.0: -4
500.0,554.0,344.0,365.0: At:
587.0,623.0,346.0,366.0: ?g;
663.0,699.0,344.0,365.0: w},
719.0,833.0,344.0,370.0: w‘w?’?.
851.0,852.0,354.0,357.0: ‘
869.0,887.0,349.0,367.0: f
408.0,414.0,378.0,382.0: H
456.0,497.0,367.0,386.0: 1
844.0,907.0,358.0,386.0: LA],
961.0,980.0,366.0,379.0: “a
386.0,397.0,385.0,398.0: y
417.0,472.0,371.0,427.0: g
795.0,797.0,396.0,398.0: .
831.0,854.0,386.0,396.0: f
866.0,888.0,383.0,399.0: M
906.0,924.0,390.0,406.0: gr
942.0,958.0,379.0,399.0: m“
413.0,418.0,399.0,408.0: ‘
856.0,878.0,406.0,418.0: A;
887.0,913.0,403.0,419.0: ;»
919.0,958.0,402.0,421.0: mi"
964.0,1003.0,403.0,419.0: 1&4‘
402.0,464.0,425.0,443.0: £4.43
488.0,553.0,400.0,469.0: 0
850.0,970.0,416.0,440.0: ”nffv’??'ér’
991.0,993.0,423.0,426.0: ‘
331.0,333.0,521.0,524.0: ,
383.0,408.0,514.0,525.0: \,-'~
435.0,436.0,515.0,518.0: ‘
875.0,895.0,515.0,526.0: ff
923.0,929.0,518.0,523.0: «
956.0,980.0,516.0,526.0: e,
350.0,368.0,531.0,547.0: o
434.0,442.0,529.0,546.0: a
593.0,605.0,532.0,544.0: a
871.0,936.0,524.0,545.0: ?ux
966.0,986.0,525.0,538.0: U
394.0,418.0,576.0,603.0: ‘f
461.0,463.0,581.0,583.0: '
877.0,890.0,570.0,602.0: 1L
926.0,928.0,587.0,589.0: -
948.0,969.0,574.0,599.0: Ian
0.0,238.0,601.0,618.0: _
406.0,433.0,600.0,618.0: ;\
460.0,468.0,598.0,614.0: ‘~
974.0,1018.0,606.0,616.0: ')~

i think this picture is easy to recognize(white text on black background).

Solution

  • I would start here https://github.com/tesseract-ocr/tesseract/wiki/ImproveQuality get the resulting image that tesseract optimized and used for the OCR and see if there are any obvious faults with it, then if there are try to correct it. It may be doing an optimization that actually is harder to read.