Jaehwa Park

Efficient word segmentation driven by unconstrained handwritten phrase recognition

An efficient system which finds the best match between an input image and a lexicon is presented. To capture writing style of spacing between words and characters prime stroke analysis based on statistical methods is introduced. A method for estimating bound on number of characters without actual recognition is also presented. For system efficiency, before actual recognition, classified groups of word segments and eligible subset of lexicons are generated as hypotheses. The hypotheses are verified and ordered by a lexicon driven word recognizor. We have tested our approach in the street name recognition/interpretation for US mail stream. Experimental results and encouraging.

OCR in a hierarchical feature space

This paper describes a methodology that allows fast and accurate character recognition while keeping the dimensionality of the feature space relatively small. Higher dimensionality can add to the discriminatory power of a recognizer but pays the price in an increase of computational time. We present a method that achieves high accuracy even with a low-dimensional feature space by simulating a multiresolution feature space. Our approach is supported by promising experimental results. Recognition rate of 98% is achieved on a test set of about 16,000 handwritten numerals. Recognition rates on upper and lower case handprinted characters is about 95%.