Intelligent document capturing and blending system based on robust feature matching with an active camera
Wan-Yu Chen, Jialin Chen, Yu-Chi Su, Liang‐Gee Chen · 2013
We propose an intelligent document capturing and blending system based on robust feature matching for efficient document management. The proposed system not only supports handwritten text and figure extraction, but also provides image blending mechanism to automatically merge the extracted handwritten texts and figures into electronic documents for the user. The proposed system addresses camera shake and luminance variation problems caused by active cameras. Besides, we adopt robust feature matching techniques to improve the system accuracy. Experimental results show that our system supports 95.65% detection rate and achieves 88.3% compression ratio reduction compared with the previous work. Besides, we also compare system performance considering Scale Invariant Feature Transform (SIFT) [1] and Speeded-Up Robust Features (SURF) [2]. We derive 71.2% complexity reduction and 4.3% detection rate degradation with SURF feature matching.