The success of large-scale digitization projects at museums, archives, and libraries is pushing other cultural institutions to embrace digitization to preserve their collections. By juxtaposing digital tools with digitized collections, it is now possible to study these cultural objects at a previously unknown scale. This thesis is the first attempt to explore a recently digitized children's drawings collection while developing a system to identify patterns in them linked with popular cultural objects. Artists, as young as three and as old as 25, created nearly 90,000 drawings in the span of three decades from most countries in the world. The preliminary examination unveils that these drawings mirror a solid cultural ethos by using specific iconographic subjects, objects, and colors, and the distinction between children of different parts of the globe is visible in their works. These factors not only make the dataset distinct from other sketch datasets but place it distantly from them in terms of size and multifariousness of creations and the creators. The essential and another dimension of the project is matching the drawings and the popular cultural objects they represent. A deep learning model that learns a metric to rank the visual similarity between the images is used to identify the drawing-artwork pairs. Though the networks developed for image classification perform inadequately for the matching task, networks used for pattern matching in paintings show good performance. Fine-tuning the models increases the performance drastically. The primary outcomes of this work are (1) systems trained with a few methodically chosen examples perform comparably to the systems trained on thousands of generic samples and (2) using drawings enriched by adding generic effects of watercolor, oil painting, pencil sketch, and texturizing mitigates the situation of network learning examples by heart.