Word Warping for Offline Handwriting Recognition

We present a novel method of offline whole-word handwriting recognition. We use automatic image morphing to compute 2-D geometric warps that align the strokes of each word image with the strokes of word images of training examples. Once the strokes of a given word are aligned to a training example, we use distance maps to compare how similar the two words are. Like 1-D Dynamic Programming (DP) methods, our warp-based method is robust to limited variation in word length and letter spacing. However, due to its 2-D nature, our method is also more robust than 1-D DP methods in handling variations caused by additional inconsistencies in character shape and stroke placement. Although we use DP for coarse alignment, the novel contribution of this paper is not 2-D DP, but morphing to automatically discover an actual 2-D mesh-based warp, followed by the use of distance maps to compute similarity between words. Early results are encouraging. On two datasets (1,000 training and 1,000 test words each), we get 88.77% and 89.33% recognition accuracy for in-vocabulary words. These are increases of 7.89% and 17.16% above the results of a 1-D DP approach.