The Segmentation and Identification of Handwriting in Noisy Document Images
DAS '02 Proceedings of the 5th International Workshop on Document Analysis Systems V
Machine Printed Text and Handwriting Identification in Noisy Document Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the 2010 ACM Symposium on Applied Computing
A robust two level classification algorithm for text localization in documents
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
Optical font recognition of chinese characters based on texture features
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
| Hi-index | 0.00 |
In applications of character recognition where machine-printed and hand-written characters are involved, it is important to know if the character image, or the whole word, is machine- or hand-written. This is due to the accuracy difference between the algorithms and systems oriented to machine- or handwritten characters. Obviously, this type of knowledge leads to the increase of the overall system quality. In this work a classification system is presented which reads a raster image of a character and outputs two confidence values, one for "machine-written" and one for "hand-written" character classes, respectively. The proposed system features a preprocessing step, which transforms a general uncentered character image into a normalized form, then the feature extraction phase extracts relevant information from the image, and at the end, a standard classifier based on a feedforward neural network creates the final response. At the end, some results on a proprietary image database are reported.