Segmentation and Identification of Handwriting in Noisy Documents

In this paper we present an approach to the problem of segmenting and identifying handwritten annotations in noisy document images. In many types of documents such as correspondence, it is not uncommon for handwritten annotations to be added as part of a note, correction, clarification, or instruction, or a signature to appear as an authentication mark. It is important to be able to segment and identify such handwriting so we can 1) locate, interpret and re-trieve them efficiently in large document databases, and 2) use different algo-rithms for printed/handwritten text recognition and signature verification. Our approach consists of two processes: 1) a segmentation process, which divides the text into regions at an appropriate level (character, word, or zone), and 2) a classification process which identifies the segmented regions as handwritten. To determine the approximate region size where classification can be reliably per-formed, we conducted experiments at the character, word and zone level. We found that the reliable results can be achieved at the word level with a classifi-cation accuracy of 97.3%. The identified handwritten text is further grouped into zones and verified to reduce false alarms. Experiments show our approach is promising and robust.