Object Detection Based Handwriting Localization
Yuli Wu, Yucheng Hu, Suting Miao

TL;DR
This paper introduces a deep learning method using object detection to accurately localize handwritten regions in documents, improving anonymization and potentially aiding handwriting recognition and signature verification.
Contribution
The paper proposes a novel CNN-based approach using Cascade R-CNN for fast and generalizable handwritten region detection in documents, enhancing privacy protection.
Findings
Works at 10 fps on GPU during inference.
Generalizes well across languages and unseen document types.
Facilitates further handwriting-related tasks.
Abstract
We present an object detection based approach to localize handwritten regions from documents, which initially aims to enhance the anonymization during the data transmission. The concatenated fusion of original and preprocessed images containing both printed texts and handwritten notes or signatures are fed into the convolutional neural network, where the bounding boxes are learned to detect the handwriting. Afterwards, the handwritten regions can be processed (e.g. replaced with redacted signatures) to conceal the personally identifiable information (PII). This processing pipeline based on the deep learning network Cascade R-CNN works at 10 fps on a GPU during the inference, which ensures the enhanced anonymization with minimal computational overheads. Furthermore, the impressive generalizability has been empirically showcased: the trained model based on the English-dominant dataset…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsHandwritten Text Recognition Techniques · Vehicle License Plate Recognition · Advanced Neural Network Applications
