We present a novel light field dataset along with a real-time and scale-invariant object recognition system. Our method is based on bag-of-visual-words and codebook approaches. Its evaluation was carried out on a subset of our dataset of unconventional images. We show that the low variance in scale inferred from the specificities of a plenoptic camera allows high recognition performance. With one training image per object to recognise, recognition rates greater than 90 % are demonstrated despite a scale variation of up to 178 %. Our versatile light-field image dataset, CSEM-25, is composed of five classes of five instances captured with the recent industrial Raytrix R5 camera at different distances with several poses and backgrounds. We make it available for research purposes.