RWIL: Robust Writer Identification for Indic Language
Babu Kumar, Parveen Kumar, Ambalika Sharma · 2018
Writer Identification plays an important role in fraud detection while considering the case of unauthorized access to banks and other security checks. Indic Forensic document analysis is also done in Devanagari handwritten languages. We have proposed a robust model for writer identification for Indic languages. It is complex to efficiently extract the words and characters from the Devanagari handwritten document because of overlapping, compound characters, modifiers and touching, etc. The proposed model is efficient for recognizing and classifying data because of its feature extraction and training at different convolution and pooling stages. We have prepared Devanagari (Hindi) dataset of 80 students. The proposed model is trained by using the prepared Hindi dataset and it is not require any domain knowledge for handwriting recognition. The experiments are done on three different languages (Hindi, Kannada and Arabic language) and obtained satisfactory results.