Exploration of Novel approaches for offline writer identification using handwritten words

Abstract

This thesis presents innovative approaches for offline handwritten word image author identification, leveraging various deep learning techniques. The first work employs feature maps from pre-trained CNN layers to capture writer-specific characteristics. Key-point regions are first detected using the SIFT algorithm across different abstractions like characters and their combinations. These regions are processed through a CNN, producing feature maps that are then represented using a modified HOG feature descriptor. A unique contribution lies in extracting additional cues from these feature maps through a saliency measure derived using Sparse Principal Component Analysis (SPCA). The saliency scores are integrated with HOG features to create customized descriptors, which are then classified using SVMs to determine the identity of the writer.