Deep Capsule Network based Automatic Batch Code Identification Pipeline for a Real-life Industrial Application

Chandan Kumar Singh, Vivek Kumar Gangwar, Harsh Vardhan Singh, Karan Narain, Anima Majumder, Swagat Kumar

Research output: Chapter in Book/Report/Conference proceedingConference proceeding (ISBN)

Abstract

Automatic recognition of text, such as a batch code printed on a box placed on a moving conveyor belt, is still a challenging problem. This paper proposes an end-to-end character recognition technique while addressing the major challenges encountered in a real environment, such as motion blur in the acquired images, slanted or oriented characters, creased batch codes due to wear and tear of boxes, variations in label formats, and variations in printing styles. The major contribution of this work lies in development of three sequential modules: text localization using Connectionist Text Proposal Network(CTPN), character detection and character recognition using a modified version of the capsule network (CapsNet). In contrast to CapsNet, where only a standard single convolution is used, the proposed method uses a series of feature blocks, making it a deep CapsNet which is later proven to generate more comprehensive and better separable feature vectors over its counterpart. The feature generation module is further enhanced by setting a smaller kernel size than CapsNet. The proposed system is validated on a real-world box / packet dataset generated in a retail manufacturing industry. The proposed recognition network architecture is also validated on a standard public dataset (ICDAR 2013). The comparative results are presented with statistical analysis in the experimental results section.

Original languageEnglish
Title of host publication2019 International Joint Conference on Neural Networks, IJCNN 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728119854
DOIs
Publication statusPublished - 19 Jul 2019
Event2019 International Joint Conference on Neural Networks, IJCNN 2019 - Budapest, Hungary
Duration: 14 Jul 201919 Jul 2019

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2019-July

Conference

Conference2019 International Joint Conference on Neural Networks, IJCNN 2019
CountryHungary
CityBudapest
Period14/07/1919/07/19

Keywords

  • character recognition
  • conveyors
  • feature extraction
  • image recognition
  • statistical analysis
  • text analysis

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    Singh, C. K., Gangwar, V. K., Singh, H. V., Narain, K., Majumder, A., & Kumar, S. (2019). Deep Capsule Network based Automatic Batch Code Identification Pipeline for a Real-life Industrial Application. In 2019 International Joint Conference on Neural Networks, IJCNN 2019 [8852303] (Proceedings of the International Joint Conference on Neural Networks; Vol. 2019-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2019.8852303