A component-tree based method for user-intention guided text extraction

We propose a new component-tree based method with efficient and effective pruning strategies for user intention guided text extraction from scene images. A grayscale image is represented first as two component-trees, whose nodes represent possible candidates of character components. The non-text candidates are then pruned by using contrast, geometric and text line information as well as the constraint imposed by user intention. Bounding boxes of the intended words are then generated by grouping the survived text objects. Finally, the better result is chosen as extracted text from the two trees using an evaluation function. The proposed algorithm is evaluated on ICDAR-2003 benchmark dataset and promising results are achieved.