Aspect Ratio Adaptive Normalization for Handwritten Character Recognition
ICMI '00 Proceedings of the Third International Conference on Advances in Multimodal Interfaces
Writer Identification Using Edge-Based Directional Features
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Off-line Handwriting Identification Using HMM Based Recognizers
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Automatic Writer Identification Using Fragmented Connected-Component Contours
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Combining Character Classifiers Using Member Classifiers Assessment
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
CAIP'05 Proceedings of the 11th international conference on Computer Analysis of Images and Patterns
Offline text-independent writer identification using codebook and efficient code extraction methods
Image and Vision Computing
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In this paper, two level handwriting recognition concept is presented, where writer identification is used in order to increase handwriting recognition accuracy. On the upper level, author identification is performed. Lower level consists of a classifiers set trained on samples coming from individual writers. Recognition from upper level is used on the lower level for selecting or combining classifiers trained for identified writers. The feature set used on the upper level contains directional features as well as the features characteristic for general writing style as line spacing, tendency to line skewing and proportions of text line elements, which are usually lost in typical process of handwritten text normalization. The proposed method can be used in applications, where texts subject to recognizing come form relatively small set of known writers.