Watermark Text Pattern Spotting in Document Images

• URL: http://arxiv.org/abs/2401.05167v2
• Date: Thu, 11 Jan 2024 10:32:49 GMT
• Title: Watermark Text Pattern Spotting in Document Images
• Authors: Mateusz Krubi\'nski, Stefan Matcovici, Diana Grigore, Daniel Voinea and Alin-Ionut Popa
• Abstract summary: In the wild, writing can come in various fonts, sizes and forms, making generic recognition a very difficult problem. We propose a novel benchmark (K-Watermark) containing 65,447 data samples generated using Wrender. A validity study using humans raters yields an authenticity score of 0.51 against pre-generated watermarked documents.
• Score: 3.6298655794854464
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Watermark text spotting in document images can offer access to an often unexplored source of information, providing crucial evidence about a record's scope, audience and sometimes even authenticity. Stemming from the problem of text spotting, detecting and understanding watermarks in documents inherits the same hardships - in the wild, writing can come in various fonts, sizes and forms, making generic recognition a very difficult problem. To address the lack of resources in this field and propel further research, we propose a novel benchmark (K-Watermark) containing 65,447 data samples generated using Wrender, a watermark text patterns rendering procedure. A validity study using humans raters yields an authenticity score of 0.51 against pre-generated watermarked documents. To prove the usefulness of the dataset and rendering technique, we developed an end-to-end solution (Wextract) for detecting the bounding box instances of watermark text, while predicting the depicted text. To deal with this specific task, we introduce a variance minimization loss and a hierarchical self-attention mechanism. To the best of our knowledge, we are the first to propose an evaluation benchmark and a complete solution for retrieving watermarks from documents surpassing baselines by 5 AP points in detection and 4 points in character accuracy.

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