Base line correction for handwritten word recognition

Abstract

The authors have researched two-letter state name (state name abbreviation) recognition and full state name recognition. According to this research, they think that the accuracy of the character segmentation is essential to recognize the word correctly, and it depends on the normalization of the word image. The normalization includes smoothing, underline removal, spurious blob removal, slant and base line correction etc. They present a new base line correction algorithm for the off-line handwritten words, which include cursive (continuous or running) words and hand-printed words. It uses background region analysis with the lower convex hull which is background area closed for three directions (upper, right, left), and the upper and bottom profiles of the merged convex hull. The authors show that the new method of base line correction is very powerful for most word images for city names of the USPS mail address database. The resulting image is useful for the holistic approach, and it's effective even when the image includes parts under the base line, for example, f, g, j, or a very large character.