XII

Preprocessing text is one real-world application for the image processing techniques that we have previously learned. For this activity, I have binarized handwritten text and performed template matching to find words within the document.

Figure 1. Original image

Figure 2. Region of Interest

Figure 3. 2D Fourier Transform of Fig. 2

Figure 4. Filtered Fourier Transform of Fig. 3

Figs. 2-3 show my attempt in removing the lines from the image. Due to the low resolution and noise of the original image, filtering in the frequency domain did not fully remove the line in the image. For me to continue to the binarization of the text, I manually removed the remaining gray-ish parts.

To facilitate the binarization, morphological transformation was implemented through the close and skel operations.

Figure 4. After operating close

Figure 5.  After operating skel() on Fig. 4

Fig. 5 shows the final preprocessed image. The "M" and "O" cannot be identified properly, but the "D", "E" and the "III" are still good. The quality of binarization is of course dependent on the quality of the original handwritten text. Due to the thinning strokes, the letters "M" and "O" were damaged by the processing.

Figure 6. Template

To end this, I finally do template correlation. Using imcorrcoef(), I tried to find other instances of the word "DESCRIPTION" in the image (Fig. 1). However, I was only able to find the same spot where my template came from.

Figure 7. imcorrcoef() result. 

Fig. 7 shows the result. Notice the white dot in the right side. This is the approximated location of the match of the used template from imcorrcoef().


  Self-Assessment: 7/10