Finally, as seen in Figure 2 , text characters are extracted from this binarized image using Tesseract, an open-source OCR engine. Originally developed by Hewlett Packard and presented in by S. Rice et al. The input, a colour image captured using a smart-phone, is first sent for HSV separation, a technique already in use in meteorology  and medicine .
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For each dataset, a separate HSV range was used, as follows:. The output generated here is an image with everything blacked out, except the signboard s , which are retained by virtue of their colour.
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This filtered image is then sent for automatic zooming. Here, the image is cropped, leaving behind an output with only the signs in it. The first step is to perform Canny filtering on the image, to detect the edges of the ROIs in it.
After this contour detection, binary dilation is done in the horizontal and vertical directions, and a corresponding bounding box created, that is expected to overlap with the text area. This area is then cropped out and optimally resized, while conserving the aspect ratio of the cropped area. Hence, the text area is segregated from the remaining background, making it ready for binarization and subsequent OCR conversion. This technique is useful for converting a grayscale image into a version of less wider intensity range of the image pixels.
There are many segmentation techniques which are utlized for the same. Here, the character areas are shaded black, while the rest is coloured white, irrespective of the original polarities of the foreground and background shades. This makes it apt for our workflow; hence, it has been included after the perspective correction stage. Figure 2.
Workflow for extracting text characters from images. The image is first divided into its component R red , G green and B blue channels, and Canny edge detection is applied on each of them.
The aspect ratios of the EBs obtained are limited to a range between 0. This filters out the obvious non-text areas. Situations may arise where an EB has one or more EBs inside it, since both the internal and external boundaries of the characters are detected. If an EB completely encloses one or two EBs, these internal EBs may be ignored, since they correspond to the internal boundaries of the text characters.
However, if the number of EBs enclosed are more than two, only the internal EBs are preserved; the external EB is ignored, as this component would not correspond to a text character.
These constraints help retain all the text-like elements, while removing the non-text elements. As a result, only the preserved EBs are carried forward for binarization. For this, the foreground and background intensities of each EB is estimated.
Taking the estimated foreground intensity as the threshold, each EB is binarized, assuming that each character is uniformly coloured. Inversion of each binarized output BW EB is carried out in order to colour the foreground text black, and the background white. This depends on whether the intensity of the foreground is higher or lower than that of the background. In other words. For the final stage, the Tesseract OCR engine is used.pl.nadaqyra.cf
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This detects and extracts characters from the HSV filtered, zoomed and binarized image, and saved to a text file. Although it may be used to detect characters in multiple languages, we have restricted Tesseract to look only for characters in English, and generate a text file corresponding to the image. Our method works well on most images in each dataset. As seen in Figure 3 , most of the images in both datasets are properly filtered by their colour, and zoomed properly.
The automatic zooming helps in minimizing the number of ghost characters, which are background elements that would otherwise be detected as characters. Figure 3. One is a printer. The other is a PC. Images and texts info can be stored and searched later when necessary.
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Comparison of Serial Number Composite note is a counterfeit made of pieces of genuine notes. Demo Videos.
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