Name
Abstract | ||
|---|---|---|
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 retrieve them efficiently in large document databases, and 2) use different algorithms 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 performed, 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 classification 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. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1007/3-540-45869-7_12 | Document Analysis Systems |
Keywords | Field | DocType |
zone level,word level,appropriate level,large document databases,handwritten annotation,handwritten text,noisy document images,noisy document image,classification accuracy,handwritten text recognition,classification process | Authentication,Market segmentation,Character recognition,Handwriting,Pattern recognition,Segmentation,Computer science,Image segmentation,Gabor filter,Artificial intelligence,Text recognition | Conference |
Volume | ISSN | ISBN |
2423 | 0302-9743 | 3-540-44068-2 |
Citations | PageRank | References |
15 | 1.09 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yefeng Zheng | 1 | 1391 | 114.67 |
| Huiping Li | 2 | 98 | 12.58 |
| David Doermann | 3 | 4313 | 312.70 |