Character Recognition by Deep Learning: An Enterprise solution

Khaled Bouaziz; Thiagarajan Ramakrishnan; Srinivasan Raghavan; Kyle Grove; Awny Al-Omari; Choudur Lakshminarayan

doi:10.1109/BigData.2018.8622465

Character Recognition by Deep Learning: An Enterprise solution

Khaled Bouaziz
, Thiagarajan Ramakrishnan
, Srinivasan Raghavan
, Kyle Grove
, Awny Al-Omari
, Choudur Lakshminarayan

School of Business

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

11 Scopus citations

Abstract

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.

Original language	English
Title of host publication	Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
Editors	Naoki Abe, Huan Liu, Calton Pu, Xiaohua Hu, Nesreen Ahmed, Mu Qiao, Yang Song, Donald Kossmann, Bing Liu, Kisung Lee, Jiliang Tang, Jingrui He, Jeffrey Saltz
Pages	1719-1727
Number of pages	9
ISBN (Electronic)	9781538650356
DOIs	https://doi.org/10.1109/BigData.2018.8622465
State	Published - 2 Jul 2018
Event	2018 IEEE International Conference on Big Data, Big Data 2018 - Seattle, United States Duration: 10 Dec 2018 → 13 Dec 2018

Publication series

Name	Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018

Conference

Conference	2018 IEEE International Conference on Big Data, Big Data 2018
Country/Territory	United States
City	Seattle
Period	10/12/18 → 13/12/18

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

Character Recognition
Convolutional Neural Networks
Image Processing
Probabilistic auto correction
System integration

Access to Document

10.1109/BigData.2018.8622465

Cite this

Bouaziz, K., Ramakrishnan, T., Raghavan, S., Grove, K., Al-Omari, A., & Lakshminarayan, C. (2018). Character Recognition by Deep Learning: An Enterprise solution. In N. Abe, H. Liu, C. Pu, X. Hu, N. Ahmed, M. Qiao, Y. Song, D. Kossmann, B. Liu, K. Lee, J. Tang, J. He, & J. Saltz (Eds.), Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018 (pp. 1719-1727). Article 8622465 (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018). https://doi.org/10.1109/BigData.2018.8622465

Bouaziz, Khaled ; Ramakrishnan, Thiagarajan ; Raghavan, Srinivasan et al. / Character Recognition by Deep Learning : An Enterprise solution. Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018. editor / Naoki Abe ; Huan Liu ; Calton Pu ; Xiaohua Hu ; Nesreen Ahmed ; Mu Qiao ; Yang Song ; Donald Kossmann ; Bing Liu ; Kisung Lee ; Jiliang Tang ; Jingrui He ; Jeffrey Saltz. 2018. pp. 1719-1727 (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018).

@inproceedings{57a626fa91e2468795db96b932ac15e1,

title = "Character Recognition by Deep Learning: An Enterprise solution",

abstract = "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.",

keywords = "Character Recognition, Convolutional Neural Networks, Image Processing, Probabilistic auto correction, System integration",

author = "Khaled Bouaziz and Thiagarajan Ramakrishnan and Srinivasan Raghavan and Kyle Grove and Awny Al-Omari and Choudur Lakshminarayan",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Big Data, Big Data 2018 ; Conference date: 10-12-2018 Through 13-12-2018",

year = "2018",

month = jul,

day = "2",

doi = "10.1109/BigData.2018.8622465",

language = "English",

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booktitle = "Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018",

}

Bouaziz, K, Ramakrishnan, T, Raghavan, S, Grove, K, Al-Omari, A & Lakshminarayan, C 2018, Character Recognition by Deep Learning: An Enterprise solution. in N Abe, H Liu, C Pu, X Hu, N Ahmed, M Qiao, Y Song, D Kossmann, B Liu, K Lee, J Tang, J He & J Saltz (eds), Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018., 8622465, Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018, pp. 1719-1727, 2018 IEEE International Conference on Big Data, Big Data 2018, Seattle, United States, 10/12/18. https://doi.org/10.1109/BigData.2018.8622465

Character Recognition by Deep Learning: An Enterprise solution. / Bouaziz, Khaled; Ramakrishnan, Thiagarajan; Raghavan, Srinivasan et al.
Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018. ed. / Naoki Abe; Huan Liu; Calton Pu; Xiaohua Hu; Nesreen Ahmed; Mu Qiao; Yang Song; Donald Kossmann; Bing Liu; Kisung Lee; Jiliang Tang; Jingrui He; Jeffrey Saltz. 2018. p. 1719-1727 8622465 (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

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

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UR - https://www.scopus.com/pages/publications/85062629781#tab=citedBy

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 -

Bouaziz K, Ramakrishnan T, Raghavan S, Grove K, Al-Omari A, Lakshminarayan C. Character Recognition by Deep Learning: An Enterprise solution. In Abe N, Liu H, Pu C, Hu X, Ahmed N, Qiao M, Song Y, Kossmann D, Liu B, Lee K, Tang J, He J, Saltz J, editors, Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018. 2018. p. 1719-1727. 8622465. (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018). doi: 10.1109/BigData.2018.8622465

Character Recognition by Deep Learning: An Enterprise solution

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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