Abstract
Camera-based scene text detection and recognition is a research area that has attracted countless attention and had made noticeable progress in the area of deep learning technology, computer vision, and pattern recognition. They are highly recommended for capturing text on-scene images (signboards), documents with a multipart and complex background, images on thick books and documents that are highly fragile. This technology encourages real-time processing since handheld cameras are built with very high processing speed and internal memory, are quite easy and flexible to use than the traditional scanner whose usability is limited as they are not portable in size and cannot be used on images captured by cameras. However, characters captured by traditional scanners pose fewer computational difficulties as compared to camera captured images that are associated with divers’ challenges with consequences of high computational complexity and recognition difficulties. This paper, therefore, reviews the various factors that increase the computational difficulties of Camera-Based OCR, and made some recommendations as per the best practices for Camera-Based OCR systems.
Highlights
The original goal of OCR is to process document images acquired by desktop scanners
Camera-based scene text detection and recognition is a research area that has attracted countless attention and had made noticeable progress in the area of deep learning technology, computer vision, and pattern recognition. They are highly recommended for capturing text on-scene images, documents with a multipart and complex background, images on thick books and documents that are highly fragile. This technology encourages real-time processing since handheld cameras are built with very high processing speed and internal memory, are quite easy and flexible to use than the traditional scanner whose usability is limited as they are not portable in size and cannot be used on images captured by cameras
One of the major limitations of the traditional scanners is that they only deal with text on paper while the target of handheld digital cameras is not limited to just print documents; they can handle scene images as well [1]
Summary
The original goal of OCR is to process document images acquired by desktop scanners. Camera-based OCR technology is concerned with recognizing characters captured by portable devices with an attached camera be it handheld or wearable cameras It is best suited for scene images, documents with multipart and complex backgrounds since it is flexible to use than the traditional Scanner whose usability is limited as they are not portable in size. Image is poorly captured either due to operator error, incompetence, low device quality or poor environmental conditions such as lighting when a camera is used to capture the image [4, 5], because of the non-contact nature of digital cameras attached to handheld or wearable devices, the output of acquired images mostly suffer from skew and perspective distortion It is affected by complex background, blur, low resolution, non-uniform lightning, skew and perspective distortion
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