Automatic Chinese Postal Address Block Location Using Proximity Descriptors and Cooperative Profit Random Forests.

Locating the destination address block is key to automated sorting of mails. Due to the characteristics of Chinese envelopes used in mainland China, we here exploit proximity cues in order to describe the investigated regions on envelopes. We propose two proximity descriptors encoding spatial distributions of the connected components obtained from the binary envelope images. To locate the destination address block, these descriptors are used together with cooperative profit random forests (CPRFs). Experimental results show that the proposed proximity descriptors are superior to two component descriptors, which only exploit the shape characteristics of the individual components, and the CPRF classifier produces higher recall values than seven state-of-the-art classifiers. These promising results are due to the fact that the proposed descriptors encode the proximity characteristics of the binary envelope images, and the CPRF classifier uses an effective tree node split approach

Pincode detection using deep CNN for postal automation

© 2017 IEEE. 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