Abstract
Tone-Mapping Operators (TMOs) are usually used to produce Low Dynamic Range (LDR) versions of the High Dynamic Range (HDR) images that encapsulate better perceptual quality. Many TMOs have been proposed in literature as displaying HDR images on LDR screen is one major requirement, as long as LDR displays are in use. On the other hand, there are also many high performance current Computer Vision System (CVS) applications in use such as those for Optical Character Recognition (OCR), Automated Driver Assistance System (ADAS), etc. that are based on processing LDR images. These are incapable of processing the vast dynamic range of HDR imaging. Many times performance of these systems is excellent when images with optimal illumination are used and degrade badly for underexposed or overexposed images. HDR imaging do provide the avenue to combat non-optimal lighting condition and generate LDR images that maximizes image features and quality that is best suited for the targeted CVS application. In this work we have used a high performance state-of-the-art TMO (ATT) algorithm and has designed an automated training based mechanism to tune its parameters for optimizing the performance of a popular and free online available OCR engine Tesseract. Here we focus on a specific scenario of vision-based industrial product inspection. LDR imaging in a batch processing of product-line may suffer from glare, light source in background or poor illumination due to variation in position, orientation and movement of the product on conveyor belt based system with respect to camera and light source. HDR imaging can help reduce false rejection if optimal LDR images can be obtained for text reading by the OCR engine. Our proposed learning-based Application Specific TMO (ASTMO) significantly improves the OCR accuracy for diverse illumination conditions. Though we are reporting early results but they can pave the way for further research in this direction.