TY - GEN
T1 - Character Recognition by Deep Learning
T2 - 2018 IEEE International Conference on Big Data, Big Data 2018
AU - Bouaziz, Khaled
AU - Ramakrishnan, Thiagarajan
AU - Raghavan, Srinivasan
AU - Grove, Kyle
AU - Al-Omari, Awny
AU - Lakshminarayan, Choudur
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - Ease-of-use analytics at scale is the holy grail of industrial strength machine learning. While there have been advances in APIs, algorithms, and user interfaces, building an end to end work flow involving data ingestion, data preparation, model training, model scoring, and visualization received limited investment and effort producing only marginal innovation. This paper outlines a proof of concept that demonstrates an analytical workflow that integrates multiple analytical tools and techniques for image recognition. The solution combines Relational Databases and Machine Learning (teradata), Deep Learning (TensorFlow), Distributed File System (HDFS), Graphical Processing Units, and user interface tools over a communication fabric (teradata QueryGrid). In particular, we demonstrate hand written word recognition through an application of Convolutional Neural Networks in TensorFlow and teradata's custom analytical functions to recognize first names and last names in the payee field section of financial negotiable instruments such as in a cheque. We hope that this paper will serve as a guide to successful implementation of analytical workflows in production.
AB - Ease-of-use analytics at scale is the holy grail of industrial strength machine learning. While there have been advances in APIs, algorithms, and user interfaces, building an end to end work flow involving data ingestion, data preparation, model training, model scoring, and visualization received limited investment and effort producing only marginal innovation. This paper outlines a proof of concept that demonstrates an analytical workflow that integrates multiple analytical tools and techniques for image recognition. The solution combines Relational Databases and Machine Learning (teradata), Deep Learning (TensorFlow), Distributed File System (HDFS), Graphical Processing Units, and user interface tools over a communication fabric (teradata QueryGrid). In particular, we demonstrate hand written word recognition through an application of Convolutional Neural Networks in TensorFlow and teradata's custom analytical functions to recognize first names and last names in the payee field section of financial negotiable instruments such as in a cheque. We hope that this paper will serve as a guide to successful implementation of analytical workflows in production.
KW - Character Recognition
KW - Convolutional Neural Networks
KW - Image Processing
KW - Probabilistic auto correction
KW - System integration
UR - http://www.scopus.com/inward/record.url?scp=85062629781&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062629781&partnerID=8YFLogxK
U2 - 10.1109/BigData.2018.8622465
DO - 10.1109/BigData.2018.8622465
M3 - Conference contribution
AN - SCOPUS:85062629781
T3 - Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
SP - 1719
EP - 1727
BT - Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
A2 - Abe, Naoki
A2 - Liu, Huan
A2 - Pu, Calton
A2 - Hu, Xiaohua
A2 - Ahmed, Nesreen
A2 - Qiao, Mu
A2 - Song, Yang
A2 - Kossmann, Donald
A2 - Liu, Bing
A2 - Lee, Kisung
A2 - Tang, Jiliang
A2 - He, Jingrui
A2 - Saltz, Jeffrey
Y2 - 10 December 2018 through 13 December 2018
ER -