Photo Digitizer Software

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Photo Digitizing software for CAD/CAM systems were invented by N-hega Technology in 2001. This technology relies on artificial intelligence to automatically digitalize patterns and templates to vector files in a matter of seconds. Photo Digitizer Software requires a 1-) software for digitizing images and 2-) a camera or scanner to capture the photo image of the template. Learn more about our NScan Scanner and NShot-Pro camera digitizer here: https://www.n-hega.com/digitizing-nesting-products

 

Background:

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A product is generally made by putting together a number of pieces of patterns together. A design of a product is determined by the shapes of these pieces put together. Photo digitizers for the manufacturing industry helps physical templates created by hand be digitized to a vector format automatically in a matter of seconds.

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How do Photo Digitizer Software work:

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Stage 1: Scan

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The first stage scans the physical pattern into a raster image. This can be done with any of current digital imaging techniques. For instance, a flatbed scanner commonly seen in offices, or a CCD digital camera. In an industrial setting a large-format scanner might be used. The result is a raster image, i.e., a digital facsimile of the physical pattern.

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Stage 2: Recognition

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Given a raster image of the pattern from the first stage, the method extracts relevant information from it. The single most important information about the pattern is its outline. Other important features include lines and curves drawn on the pattern, which we call internal curves hereafter. Both the outline and the internal curves appear in the raster image as curves. Therefore, the method recognizes curves in the raster image. There is more than one conceivable algorithm to detect and recognize curves. Any algorithm that robustly recognizes curves in the raster image can be used for the present invention. Such an algorithm finds characteristic pixels in the raster image that are positioned like a curve. What characterizes such a pixel depends on what kind of curves the algorithm is looking for. In the case the color of the background and the pattern paper are known, a pixel on the outline of the pattern is characterized as a boundary of the two colors. A pixel on an internal curve is characterized by its color different from the pattern paper color. Though such simple characterizations by themselves are not enough, they serve as local criteria to narrow down the locus of the curves. Having found a set of candidate pixels that satisfy the local criteria, the algorithm finds curves that lie on such pixels. The result of this stage is a set of data, which we call a digital pattern. It comprises the representation of curves that constitute the outline and the internal curves. The representation is such that the coordinates of successive points on the curves can be readily calculated. Additionally, the digital pattern may include other accompanying data such as an identification number, date of production, and what kind of fabric should be used, which can be entered to the system manually. It may even include the original raster image so that, should a mistake in the second stage be discovered later, the recognition can be redone, perhaps with a different set of parameters.

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Stage 3: Manual Data Input (Optional)

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Each pattern has some accompanying data such as an identification number, date of production, and kind of fabric that should be used. This can be entered by an operator manually. Some of the accompanying data is written on the physical pattern. For instance, grading information sometimes is represented as numbers written on the pattern near points on the scanner. In FIG. 1, one of the patterns shown  is an example of this. White arrows indicate the handwritten numbers that represent important information. While it would be best if it could be machine-read, it might be technically difficult to reliably recognize all the information scattered over the pattern. Instead, all these data can be input by an operator. To facilitate this, the system can show the raster image of the pattern on the computer screen so that the operator can read the data off the screen. It can even move the part of the image from showing one number to another when inputting the grading information.

 

Link to patent: https://patents.google.com/patent/US7031527B2/en