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OCR stands for Optical Character Recognition. It is the process of identifying alphanumeric characters in an image. It extracts and repurposes data from scanned documents and converts into machine-readable text. The following steps outline how OCR works:
Images can be obtained using scanning tools or cameras.
Pre-processing of images is mostly done to ensure that computer systems have an easier time identifying characters in an image. A wide range of pre-processing algorithms can be implemented based on requirements. Some of these algorithms include:
Pre-processing is mainly conducted to enhance the image.
The next step following pre-processing is actual character recognition. One of the most basic algorithms of character recognition is pattern matching. The image is compared to a stored sample (glyph) and compared on a pixel by pixel basis.
Pattern matching isolates a glyph and compares it with a similarly stored glyph. Noticeably, pattern recognition works only in those cases where the stored glyph has a font with similar font and scale to the input glyph. Because of this, the method works best with scanned images of documents that rely on standard fonts.
The second type of algorithm uses feature extraction, a method that breaks down the glyphs into features such as lines, closed loops, line direction, and line intersections. These features are then used to find the best match among the stored glyphs.
The confidence level is a metric that showcases how “optimistic” the algorithm is of its own prediction. This confidence level can be improved through the standard fonts and font sizes. Apart from these four basic steps, OCR accuracy can be enhanced through the implementation of application-specific optimizations.
After analysis, the system converts the extracted text data into a digital file. The file can be used to automate the completion of forms.
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