"Pincode detection using deep CNN for postal automation" by Nabin Sharma, Abira Sengupta et al.
 

Pincode detection using deep CNN for postal automation

Document Type

Conference Article

Publication Title

International Conference Image and Vision Computing New Zealand

Abstract

Postal automation has been a topic of research over a decade. The challenges and complexity involved in developing a postal automation system for a multi-lingual and multi-script country like India are many-fold. The characteristics of Indian postal documents include: multi-lingual behaviour, unconstrained handwritten addresses, structured/unstructured envelopes and postcards, being among the most challenging aspects. This paper examines the state-of-the-art Deep CNN architectures for detecting pin-code in both structured and unstructured postal envelopes and documents. Region-based Convolutional Neural Networks (RCNN) are used for detecting the various significant regions, namely Pin-code blocks/regions, destination address block, seal and stamp in a postal document. Three network architectures, namely Zeiler and Fergus (ZF), Visual Geometry Group (VGG16), and VGG M were considered for analysis and identifying their potential. A dataset consisting of 2300 multilingual Indian postal documents of three different categories was developed and used for experiments. The VGG-M architecture with Faster-RCNN performed better than others and promising results were obtained.

First Page

1

Last Page

6

DOI

10.1109/IVCNZ.2017.8402501

Publication Date

7-2-2017

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