TY - GEN
T1 - A segmentation-free recognition of two touching numerals using neural network
AU - Choi, Soon Man
AU - Oh, Il Seok
N1 - Publisher Copyright:
© 1999 IEEE.
PY - 1999
Y1 - 1999
N2 - The recognition of two touching numerals has been tackled by many researchers with the purpose of recognizing the numeric fields in many document forms. The conventional methods are based on a process with two sequential stages, viz. The segmentation of touching numerals and the recognition of the individual numerals. Due to an unlimited number of different overlapping and touching types, the segmentation-based approach has always had a limited success rate. In this paper, we propose a new segmentation-free method using a neural network. In this approach, two touching numerals are regarded as a single pattern from a pattern source with 100 classes. To obtain a training set for the neural network classifier, we synthesize the patterns by moving two isolated numerals in the NIST database horizontally until they touch. For the test set, we manually extract two touching numerals from the numeric string dataset of the NlST database. By using a modular neural network classifier, promising results have been obtained.
AB - The recognition of two touching numerals has been tackled by many researchers with the purpose of recognizing the numeric fields in many document forms. The conventional methods are based on a process with two sequential stages, viz. The segmentation of touching numerals and the recognition of the individual numerals. Due to an unlimited number of different overlapping and touching types, the segmentation-based approach has always had a limited success rate. In this paper, we propose a new segmentation-free method using a neural network. In this approach, two touching numerals are regarded as a single pattern from a pattern source with 100 classes. To obtain a training set for the neural network classifier, we synthesize the patterns by moving two isolated numerals in the NIST database horizontally until they touch. For the test set, we manually extract two touching numerals from the numeric string dataset of the NlST database. By using a modular neural network classifier, promising results have been obtained.
KW - Modular neural network classifier
KW - Segmentation-free recognition
KW - Two touching numerals
UR - https://www.scopus.com/pages/publications/79956107711
U2 - 10.1109/ICDAR.1999.791772
DO - 10.1109/ICDAR.1999.791772
M3 - Conference paper
AN - SCOPUS:79956107711
T3 - Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
SP - 253
EP - 256
BT - Proceedings of the 5th International Conference on Document Analysis and Recognition, ICDAR 1999
PB - IEEE Computer Society
T2 - 5th International Conference on Document Analysis and Recognition, ICDAR 1999
Y2 - 20 September 1999 through 22 September 1999
ER -