Simultaneous word segmentation from document images using recursive morphological closing transform

This paper describes a word segmentation algorithm which is based on the recursive morphological closing transform. The algorithm is trainable for any given document image population and is capable of detecting words on a document image simultaneously. We describe an experimental protocol to train and evaluate our word segmentation algorithm based on a set of layout ground-truthed document images. We also discussed a method to compare two sets of word bounding boxes-one from the ground truth and the other from the output of the word segmentation algorithm, and compute the numbers of miss, false, correct splitting, merging and spurious detections. The experimental results demonstrate that under the optimal algorithm parameter settings, the correct word detection percentage is about 95% on both the training and testing image populations. If this includes the splitting and merging detections, the detection percentage is about 99.4%.