Investigation of faster-RCNN inception Resnet V2 on offline Kanji handwriting characters

In recent years detection and recognition of Offline handwriting character has being a major task in the computer vision sector, researchers are looking at developing deep learning models to avoid the traditional approaches which involves the tedious task of using the conventional methods for feature extraction and localization. However, state-of-the-art object detection models rely upon region proposal algorithms as a result, they settle for object location principles, such network reduces the time period of those detection network, exposing region proposal computation as a bottleneck. Faster-RCNN is a popular model used for recognition purpose in many recognition tasks, the goal of this paper is to serve as a guide for Multi-Classification on offline Handwriting Document using Pre-trained Faster-RCNN with inception resnet v2 feature Extractor. The result obtained from the experiments shows improved pre-trained models can be used in solving the research question concerning handwriting detection and recognition.