Photographing fiscal receipts has become increasingly common with the rise of online storage and accounting services. However, capturing images in uncontrolled environments often leads to distortions that can compromise Optical Character Recognition (OCR) techniques, turning the output text unreadable. To address this problem, we propose an expert open-source filtering approach based on low-level features to identify and discard poor-quality fiscal images, select high-quality ones, and flag images that need preparation before OCR. The flagged images undergo a series of enhancement techniques, including homography transformation, super-resolution, noise reduction, sharpness adjustment, morphological operations, and binarization. Our extensive experimental evaluation, executed in a new proposed labeled dataset of fiscal receipt, shows that the proposed method lowers the average Character Error Rate metric by up to 11 points compared to baseline methods. Additionally, an ablation study reveals the impact on the accuracy of each image preparation step.