An Empirical Study on Writer Identification & Verification from Intra-variable Individual Handwriting
This addresses the problem of automated handwriting analysis for forensic or security applications, but it is incremental as it applies existing methods to a new dataset.
The paper tackles writer identification and verification in offline Bengali handwriting with high intra-variability, using handcrafted features with SVM and auto-derived features from convolutional networks, and reports interesting results from experiments on generated databases.
The handwriting of an individual may vary substantially with factors such as mood, time, space, writing speed, writing medium and tool, writing topic, etc. It becomes challenging to perform automated writer verification/identification on a particular set of handwritten patterns (e.g., speedy handwriting) of a person, especially when the system is trained using a different set of writing patterns (e.g., normal speed) of that same person. However, it would be interesting to experimentally analyze if there exists any implicit characteristic of individuality which is insensitive to high intra-variable handwriting. In this paper, we study some handcrafted features and auto-derived features extracted from intra-variable writing. Here, we work on writer identification/verification from offline Bengali handwriting of high intra-variability. To this end, we use various models mainly based on handcrafted features with SVM (Support Vector Machine) and features auto-derived by the convolutional network. For experimentation, we have generated two handwritten databases from two different sets of 100 writers and enlarged the dataset by a data-augmentation technique. We have obtained some interesting results.