{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/explanation-based-handwriting-verification","title":"Explanation based Handwriting Verification","arxiv_id":"1909.02548","date":"2019-08-14","proceeding":null,"authors":["Mihir Chauhan","Mohammad Abuzar Shaikh","Sargur N. Srihari"],"abstract":"Deep learning system have drawback that their output is not accompanied with ex-planation. In a domain such as forensic handwriting verification it is essential to provideexplanation to jurors. The goal of handwriting verification is to find a measure of confi-dence whether the given handwritten samples are written by the same or different writer.We propose a method to generate explanations for the confidence provided by convolu-tional neural network (CNN) which maps the input image to 15 annotations (features)provided by experts. Our system comprises of: (1) Feature learning network (FLN),a differentiable system, (2) Inference module for providing explanations. Furthermore,inference module provides two types of explanations: (a) Based on cosine similaritybetween categorical probabilities of each feature, (b) Based on Log-Likelihood Ratio(LLR) using directed probabilistic graphical model. We perform experiments using acombination of feature learning network (FLN) and each inference module. We evaluateour system using XAI-AND dataset, containing 13700 handwritten samples and 15 cor-responding expert examined features for each sample. The dataset is released for publicuse and the methods can be extended to provide explanations on other verification taskslike face verification and bio-medical comparison. This dataset can serve as the basis and benchmark for future research in explanation based handwriting verification. The code is available on github.","url_abs":"https://arxiv.org/abs/1909.02548v1","url_pdf":"https://arxiv.org/pdf/1909.02548v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"explanation-based-handwriting-verification","repo_url":"https://github.com/mshaikh2/HDL_Forensics","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"xai","task_name":"Explainable Artificial Intelligence (XAI)"},{"task_slug":"face-verification","task_name":"Face Verification"},{"task_slug":"handwriting-verification","task_name":"Handwriting Verification"}],"methods":[],"datasets_introduced":[{"slug":"and-dataset","name":"AND Dataset","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}