  Freehand sketches often contain sparse visual detail. In spite of the
sparsity, they are easily and consistently recognized by humans across
cultures, languages and age groups. Therefore, analyzing such sparse sketches
can aid our understanding of the neuro-cognitive processes involved in visual
representation and recognition. In the recent past, Convolutional Neural
Networks (CNNs) have emerged as a powerful framework for feature representation
and recognition for a variety of image domains. However, the domain of sketch
images has not been explored. This paper introduces a freehand sketch
recognition framework based on "deep" features extracted from CNNs. We use two
popular CNNs for our experiments -- Imagenet CNN and a modified version of
LeNet CNN. We evaluate our recognition framework on a publicly available
benchmark database containing thousands of freehand sketches depicting everyday
objects. Our results are an improvement over the existing state-of-the-art
accuracies by 3% - 11%. The effectiveness and relative compactness of our deep
features also make them an ideal candidate for related problems such as
sketch-based image retrieval. In addition, we provide a preliminary glimpse of
how such features can help identify crucial attributes (e.g. object-parts) of
the sketched objects.
