# Exposure Bracketing Techniques for Camera Document Image Enhancement
## 4 Experiments  
In order to have a more objective evaluation of improved image quality due to exposure bracketing as well as a more objective comparison of different HDR generation methods, we used two methods for comparison. First, we zoomed in different regions of the well-exposed image, corresponding to a well-exposed region, under-exposed region, and over-exposed region of the image, and we then compared these regions with the HDR images and fusion images. In the meantime, we also generated the binary images of these regions using the Sauvola method. The reason why we also considered a binary image is because it is the binary image that will be used for OCR. Figure 12 shows one of the well-exposed regions in the well-exposed image. It is very clear that in this region, the original well-exposed image has good image quality and the moderate uneven lighting will not affect the generated binary image as the Sauvola binarization method is an adaptive image processing method. The same region in the under-exposed image shows small deterioration, however this is not observable in the well-exposed image. All HDR images and fusion methods show decent results. Figure 13 shows one of the under-exposed regions in the well-exposed image. This is the situation where we can clearly see the advantage of using exposure bracketing. The under-exposed image and well-exposed image do not have decent image quality, leading to noisy binary images. All the images generated with exposure bracketing techniques lead to improved image quality. Figure 14 shows one of the over-exposed regions in the well-exposed image. For this image patch, we can see that the over-exposed image, which has good binary output in Figures 12 and 13, shows very bad results. Some textural information has been completely lost and the adaptive binarization method can no longer retrieve it. On the contrary, all the exposure bracketing methods can always retrieve good results regardless of the imaging conditions.  
Figure 11: HDR fusion method: (**a**) [PERSON] method (**b**) [PERSON]’s method.  
In order to further verify our visual observation, we began to use OCR accuracy as a criterion to justify the improved image quality due to exposure bracketing techniques. The philosophy behind this is that normally OCR accuracy is correlated with input image quality, and the better quality the document image has, the better OCR accuracy we can expect. Therefore, we set up a benchmark that was composed of 12 datasets for testing. The OCR toolkit we used is from open source Tesseract  
Figure 14: One over-exposed region in well-exposed images. From left to right: well-exposed image, under-exposed image, over-exposed image, ReinhardTMO HDR image, DurandTMO HDR image, Goshtashy fusion, and the Merten fusion method. The images in the top row correspond to RGB images and the images in the bottom row correspond to binary images.  
Figure 12: One well-exposed region in well-exposed images. From left to right: well-exposed image, under-exposed image, over-exposed image, [PERSON] HDR image, DurandTMO HDR image, Goshtashy fusion, and the Merten fusion method. The images in the top row correspond to RGB images and the images in the bottom row correspond to binary images.  
Figure 13: One under-exposed region in well-exposed images. From left to right: well-exposed image, under-exposed image, over-exposed image, ReinhardTMO HDR image, DurandTMO HDR image, Goshtashy fusion, and the Merten fusion method. The images in the top row correspond to RGB images and the images in the bottom row correspond to binary images.  
OCR [16], and the OCR accuracy was calculated based on open source ISRI Analytic Tools for OCR Evaluation [17]. The experiment results are shown in Table 1:  
From Table 1, we can clearly see the value of exposure bracketing for increasing OCR accuracy, and the best OCR result with LDR images comes from over-exposed images with 103 error characters in total. All the exposure bracketing methods increased the original LDR image's quality, leading to increased OCR accuracy.