Object Detection Based Handwriting Localization
This addresses privacy protection in document processing by enabling automated redaction of personally identifiable information, though it appears to be an incremental application of existing object detection methods.
The paper tackles the problem of localizing handwritten regions in documents to enhance anonymization during data transmission, achieving 10 fps processing speed on GPU with demonstrated generalization to unseen documents in different languages.
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.