.png){kind=icon}
Return
Offline text-independent writer identification using codebook and efficient code extraction methods
.png){kind=icon}
PRE
AI
DOI：10.1016/j.imavis.2013.03.002.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, an efficient method for text-independent writer identification using a codebook method is proposed. The method uses the occurrence histogram of the shapes in a codebook to create a feature vector for each specific manuscript. For cursive handwritings, a wide variety of different shapes exist in the connected components obtained from the handwriting. Small fragments of connected components are used to avoid complex patterns. Two efficient methods for extracting codes from contours are introduced. One method uses the actual pixel coordinates of contour fragments while the other one uses a linear piece-wise approximation using segment angles and lengths. To evaluate the methods, writer identification is conducted on two English and three Farsi handwriting databases. Both methods show promising performances with the performance of second method being better than the first one. (C) 2013 Elsevier B.V. All rights reserved.
Keywords:
Handwritten documents
Writer identification
Farsi handwriting
English handwriting
Codebook
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.2
Papers:
4.1K
Citations:
6.7K
Organization
Cited Papers
ShareSaveShareSaveShareSave
Text independent writer recognition using redundant writing patterns with contour-based orientation and curvature features.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
ShareSaveIntake of heterocyclic aromatic amines and the risk of prostate cancer in the EPIC-Heidelberg cohort
OAAI
OAAIShareSaveShareSaveShareSave
View more