CAMIO (Corpus of Annotated Multilingual Images for OCR) is a new corpus created by Linguistic Data Consortium to serve as a resource to support the development and evaluation of optical character recognition (OCR) and related technologies for 35 languages across 24 unique scripts. The corpus comprises nearly 70,000 images of machine printed text, covering a wide variety of topics and styles, document domains, attributes and scanning/capture artifacts. Most images have been exhaustively annotated for text localization, resulting in over 2.3M line-level bounding boxes. For 13 of the 35 languages, 1250 images/language have been further annotated with orthographic transcriptions of each line plus specification of reading order, yielding over 2.4M tokens of transcribed text. The resulting annotations are represented in a comprehensive XML output format defined for this corpus. The paper discusses corpus design and implementation, challenges encountered, baseline performance results obtained on the corpus for text localization and OCR decoding, and plans for corpus publication.

Delivering on the promise of real-world robotics will require robots that can communicate with humans through natural language by learning new words and concepts through their daily experiences. Our research strives to create a robot that can learn the meaning of haptic adjectives by directly touching objects. By equipping the PR2 humanoid robot with state-of-the-art biomimetic tactile sensors that measure temperature, pressure, and fingertip deformations, we created a platform uniquely capable of feeling the physical properties of everyday objects. The robot used five exploratory procedures to touch 51 objects that were annotated by human participants with 34 binary adjective labels. We present both static and dynamic learning methods to discover the meaning of these adjectives from the labeled objects, achieving average F1 scores of 0.57 and 0.79 on a set of eight previously unfelt items.