Tender Notice Extraction from E-papers Using Neural Network
Ashmin Bhattarai Anuj Sedhai Devraj Neupane Manish Khadka Rama Bastola
Tender notices are usually sought by most of the companies at regular
intervals as a means for obtaining the contracts of various projects. These
notices consist of all the required information like description of the work,
period of construction, estimated amount of project, etc. In the context of
Nepal, tender notices are usually published in national as well as local
newspapers. The interested bidders should search all the related tender notices
in newspapers. However, it is very tedious for these companies to manually
search tender notices in every newspaper and figure out which bid is best
suited for them. This project is built with the purpose of solving this tedious
task of manually searching the tender notices. Initially, the newspapers are
downloaded in PDF format using the selenium library of python. After
downloading the newspapers, the e-papers are scanned and tender notices are
automatically extracted using a neural network. For extraction purposes,
different architectures of CNN namely ResNet, GoogleNet and Xception are used
and a model with highest performance has been implemented. Finally, these
extracted notices are then published on the website and are accessible to the
users. This project is helpful for construction companies as well as
contractors assuring quality and efficiency. This project has great application
in the field of competitive bidding as well as managing them in a systematic
manner.