Abstract

We present strategies and results for identifying the symbol type (lower-case, upper-case, digit, and punctuation or special symbols) of every character in a text document by using various kinds of information from neighboring characters. In the expectation of reasonable word and character segmentation for shape clustering, we designed several type recognition methods that depend on cluster n-grams, shape codes, and withinword context. On an ASCII test corpus of 925 articles that simulates perfect image-level processing, these methods achieve a substantial improvement over default assignment of all characters to lower case.

Citations