Robust and Effective Component-Based Banknote Recognition for the Blind

Abstract

We develop a novel camera-based computer vision technology to automatically recognize banknotes for assisting visually impaired people. Our banknote recognition system is robust and effective with the following features: 1) high accuracy: high true recognition rate and low false recognition rate, 2) robustness: handles a variety of currency designs and bills in various conditions, 3) high efficiency: recognizes banknotes quickly, and 4) ease of use: helps blind users to aim the target for image capture. To make the system robust to a variety of conditions including occlusion, rotation, scaling, cluttered background, illumination change, viewpoint variation, and worn or wrinkled bills, we propose a component-based framework by using Speeded Up Robust Features (SURF). Furthermore, we employ the spatial relationship of matched SURF features to detect if there is a bill in the camera view. This process largely alleviates false recognition and can guide the user to correctly aim at the bill to be recognized. The robustness and generalizability of the proposed system is evaluated on a dataset including both positive images (with U.S. banknotes) and negative images (no U.S. banknotes) collected under a variety of conditions. The proposed algorithm, achieves 100% true recognition rate and 0% false recognition rate. Our banknote recognition system is also tested by blind users.

An approach to vision-based localisation with binary features for partially sighted people

“…Local feature descriptors are often used in vision-based object recognition [11,21], retrieval [6], or scene categorisation [38]. However, there is still a place for faster and more robust techniques, able to successfully describe and match images despite various transformations, distortions, or illumination conditions [6,12,24,25].…”

“…A computer vision approach with image matching based on Scale-Invariant Feature Transform (SIFT) [19] to localisa-tion was proposed in [23]. Another descriptor, Speeded Up Robust Features (SURF) [5], was used in a banknote recognition system for the blind by Hasanuzzaman et al [11].…”

Currency security and forensics: a survey

“…Feature selection to find an image of a banknote in any orientation is then possible using location of interest points [68,69]. A banknote image is able to be recognized in various environments, orientations and distances from the camera, tests show a 100% accuracy level.…”

The Development and Evaluation of an Eyes-Free Interaction Model for Mobile Reading Devices

“…The image is processed and text returned back to the device for the user to hear. Gaudissart et al [6], Keefer et al [9] and Hasanuzzaman et al [15] described systems in which the processing is performed on the mobile devices themselves. Although, these systems eliminated the need for dedicated servers, they required imageprocessing algorithms that have been optimized for handheld devices.…”