How AI-Powered OCR extracts Text From Images
It is still a long way from today, but you can see flashes of it in OCR (Optical Character Recognition ) technology Since its introduction, this technology has become increasingly precise and valuable as researchers and scientists uncover new applications for it Aside from translating images to text, these technologies are utilized in marketing, education, healthcare, security, and a variety of other fields. In this blog, we will understand the steps OCR follows to convert an image into text. To recognize any writing inside an image, an OCR employs three primary steps:
1. Preprocessing:
Depending on the type of identification procedure and image, some aspects may be excluded from going to the next steps.
- The picture is rotated horizontally and vertically.
- Slanting and skewing are accomplished through many methods.
- To get the average slope of the text, the Hough transformation is utilized.
- Directional histograms can also be used to calculate the horizontal and vertical slopes of the provided text.
- The picture is filtered to reduce noise.
- Thresholding is used to convert each pixel's color to black and white.
- Thinning is used to limit the number of pixels in a character to just one.
- We acquire the text skeleton by thinning.
- Thickening is also performed when needed..
2. Segmentation:
The whole preprocessed text is cut into phrases, words, and characters during the segmentation stage.
- To begin, the system cuts each word in a phrase depending on the likelihood of successive cuts.
- The best cuts define the space between words that are close together.
- If an acquired word is smeared or eliminated, the lexical analysis identifies the best likely word in its place.
- A word recognizer performs syntactic analysis using procedures.
3. Recognition:
It is the most crucial aspect of any OCR since it is here that the text is identified and extracted into digital form within a computer. It employs a variety of approaches and strategies to recognize the characters in a text. It might include any of the following methods:
- Approach to soft computing
- MLP is used for character recognition.
- Algorithm for fuzzy genetics
- Neural networks that are generic.
