Toolkit for Bar Code Recognition and Resolving on Camera Phones - Jump Starting the Internet of Things

Automatic identification technology such as RFID promises to connect physical objects with virtual representations or even computational capabilities. However, even though RFID tags are continuously falling in price, their widespread use on consumer items is still several years away, rendering large-scale experiments with such an “internet of things” difficult. Much more ubiquitous are printed bar codes, yet so far their recognition required either specialized scanner equipment, custom-tailored bar codes or costly commercial licenses – all equally significant deployment hurdles. We have developed a freely available EAN-13 bar code recognition and information system that is both lightweight and fast enough for the use on camera-equipped mobile phones, thus significantly lowering the barrier for large-scale, real-world testing of novel information and interaction applications based on “connected” physical objects. We hope that this “low tech” version of bridging the gap will allow the community to quickly develop and try out more realistic and widespread applications, and thus gain real-world experiences for better jump-starting the future internet of things, today. 1 Today’s Role of Barcode Recognition The idea of linking real-world products with virtual information has been around for quite some time. In 1998, Barrett and Maglio already described a system for attaching information to real-world objects [BM98], while 1999 Want et al. expanded upon the idea and linked arbitrary items through the use of RFID tags with both information services and actions [WFGH99]. Since then, a number of research projects have continued to explore this concept of “bridging the gap”, i.e., the automatic identification of individually tagged real-world products in order to quickly look up information or initiate a specific action [KBM02]. With the increasing mobility of powerful computing systems, e.g., mobile phones or handheld PDAs, this bridging can even be done in situ, i.e., right when we need it, where we need it. While RFID potentially offers an unprecedented user experience due to its detailed means for identification (i.e., on a per item basis) and the lack of a line-of-sight requirement for reading, most industry analysts agree that an item-level rollout (e.g., having an RFID tag on every single supermarket product) is still several years away [Jue05]. In contrast, the printed bar codes are practically ubiquitous: Virtually every item sold today carries an internationally standardized bar code on its packaging, enabling not only checkout registers to quickly sum up one’s shopping items, but also to identify a product and look up a wealth of related information. Obviously, using bar codes for linking real-world objects to virtual information has a number of drawbacks when compared to an RFID-enabled future with corresponding mobile RFID readers, such as NFC-enabled1 mobile phones. Due to their sensitivity to soiling, ripping, and lighting conditions, optical bar code recognition can be difficult. Until recently, reading a conventional (i.e., 1D) bar code inevitably required a separate laser scanner or a corresponding mobile phone scanner attachment. The increasing availability of camera phones, i.e., mobile phones with an integrated digital camera, has begun to simplify this process, however. After 2D bar codes have been successfully recognized by most consumer-grade camera phones for quite some time [Roh04], the continuously increasing quality of both the camera resolution and the employed lenses have finally made it feasible to directly read 1D bar codes with such cameras, without the need for special attachments or handheld lasers. This significantly changes the attractiveness of using barcodes for the above physical-to-digital linkage: Instead of waiting several years for a comprehensive item-level roll out of RFID tags, or forcing people to carry around specific scanner attachments for their mobile phones, the support of 1D bar code recognition on any camera phone immediately allows anybody to interact with almost any commercially available product – all it takes is a small application download. The main contribution of this paper is a freely available 1D bar code recognition toolkit that is intended to facilitate the creation of novel applications and services. We believe that the adequate performance of our recognition software, when compared with existing commercial implementations, the ease with which external data sources can be integrated, and the availability of our toolkit under an open source license will help to foster the the use of camera phones as mobile bar code scanners.