Object Detection Based Handwriting Localization

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 works well on the fictitious unseen invoices, even in Chinese. The proposed approach is also expected to facilitate other tasks such as handwriting recognition and signature verification.