TY - GEN
T1 - CNN-based Hindi numeral string recognition for Indian postal automation
AU - Zhan, Hongjian
AU - Lyu, Shujing
AU - Pal, Umapada
AU - Lu, Yue
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019
Y1 - 2019
N2 - Digits/numerals in the Indian pin-code of handwritten postal documents may touch each other and hence digit string recognition is a very challenging task. In this paper, we propose a digit string recognition system for Indian postal documents written in Hindi. Unlike normal text string, in a string of digits there is no contextual information among the digits as a digit may be followed by an arbitrary digit in a string of digits. Because of this, here we propose a new architecture which is based on CNN (Convolutional Neural Network) and CTC (Connectionist Temporal Classification), without using RNN for Hindi numeral string recognition. Also to connect CNN with CTC, we transform the outputs of CNN to a two-dimension vector to meet the feeding requirement of CTC. Furthermore, we utilize dense blocks to build CNN part to extract efficient image features. Comparative studies with the state-of-the-art methods show that the proposed method outperforms the other existing methods on Hindi numeral string recognition.
AB - Digits/numerals in the Indian pin-code of handwritten postal documents may touch each other and hence digit string recognition is a very challenging task. In this paper, we propose a digit string recognition system for Indian postal documents written in Hindi. Unlike normal text string, in a string of digits there is no contextual information among the digits as a digit may be followed by an arbitrary digit in a string of digits. Because of this, here we propose a new architecture which is based on CNN (Convolutional Neural Network) and CTC (Connectionist Temporal Classification), without using RNN for Hindi numeral string recognition. Also to connect CNN with CTC, we transform the outputs of CNN to a two-dimension vector to meet the feeding requirement of CTC. Furthermore, we utilize dense blocks to build CNN part to extract efficient image features. Comparative studies with the state-of-the-art methods show that the proposed method outperforms the other existing methods on Hindi numeral string recognition.
KW - Connectionist temporal classification
KW - Convolutional neural network
KW - Hindi numeral string
KW - Postal automation
UR - https://www.scopus.com/pages/publications/85114099147
U2 - 10.1109/ICDARW.2019.40085
DO - 10.1109/ICDARW.2019.40085
M3 - 会议稿件
AN - SCOPUS:85114099147
T3 - 2019 International Conference on Document Analysis and Recognition Workshops, ICDARW 2019
SP - 77
EP - 82
BT - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 International Conference on Document Analysis and Recognition Workshops, ICDARW 2019
Y2 - 21 September 2019 through 22 September 2019
ER -