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Beyond the Pipeline: Analyzing Key Factors in End-to-End Deep Learning for Historical Writer Identification

Hanif Rasyidi, Moshiur Farazi

TL;DR

The paper tackles historical writer identification (HWI) with end-to-end deep learning, addressing challenges from handwriting variability, degradation, and limited labels that hinder generalization to unseen writers.It systematically evaluates pre-processing, backbone architectures, and post-processing to identify design choices that improve robustness, especially in zero-shot scenarios.A SwinV2-Base backbone trained with ArcFace losses on a Text-AOI–binzarized pipeline achieves competitive performance with a simpler end-to-end design, highlighting domain-informed preprocessing as a key factor.However, ArcFace-based training incurs higher computational cost, underscoring a trade-off between robustness and efficiency for scalable deployment.The findings offer practical guidance for building robust end-to-end HWI systems and point to future work on more efficient fine-tuning and larger, diverse historical datasets.

Abstract

This paper investigates various factors that influence the performance of end-to-end deep learning approaches for historical writer identification (HWI), a task that remains challenging due to the diversity of handwriting styles, document degradation, and the limited number of labelled samples per writer. These conditions often make accurate recognition difficult, even for human experts. Traditional HWI methods typically rely on handcrafted image processing and clustering techniques, which tend to perform well on small and carefully curated datasets. In contrast, end-to-end pipelines aim to automate the process by learning features directly from document images. However, our experiments show that many of these models struggle to generalise in more realistic, document-level settings, especially under zero-shot scenarios where writers in the test set are not present in the training data. We explore different combinations of pre-processing methods, backbone architectures, and post-processing strategies, including text segmentation, patch sampling, and feature aggregation. The results suggest that most configurations perform poorly due to weak capture of low-level visual features, inconsistent patch representations, and high sensitivity to content noise. Still, we identify one end-to-end setup that achieves results comparable to the top-performing system, despite using a simpler design. These findings point to key challenges in building robust end-to-end systems and offer insight into design choices that improve performance in historical document writer identification.

Beyond the Pipeline: Analyzing Key Factors in End-to-End Deep Learning for Historical Writer Identification

TL;DR

The paper tackles historical writer identification (HWI) with end-to-end deep learning, addressing challenges from handwriting variability, degradation, and limited labels that hinder generalization to unseen writers.It systematically evaluates pre-processing, backbone architectures, and post-processing to identify design choices that improve robustness, especially in zero-shot scenarios.A SwinV2-Base backbone trained with ArcFace losses on a Text-AOI–binzarized pipeline achieves competitive performance with a simpler end-to-end design, highlighting domain-informed preprocessing as a key factor.However, ArcFace-based training incurs higher computational cost, underscoring a trade-off between robustness and efficiency for scalable deployment.The findings offer practical guidance for building robust end-to-end HWI systems and point to future work on more efficient fine-tuning and larger, diverse historical datasets.

Abstract

This paper investigates various factors that influence the performance of end-to-end deep learning approaches for historical writer identification (HWI), a task that remains challenging due to the diversity of handwriting styles, document degradation, and the limited number of labelled samples per writer. These conditions often make accurate recognition difficult, even for human experts. Traditional HWI methods typically rely on handcrafted image processing and clustering techniques, which tend to perform well on small and carefully curated datasets. In contrast, end-to-end pipelines aim to automate the process by learning features directly from document images. However, our experiments show that many of these models struggle to generalise in more realistic, document-level settings, especially under zero-shot scenarios where writers in the test set are not present in the training data. We explore different combinations of pre-processing methods, backbone architectures, and post-processing strategies, including text segmentation, patch sampling, and feature aggregation. The results suggest that most configurations perform poorly due to weak capture of low-level visual features, inconsistent patch representations, and high sensitivity to content noise. Still, we identify one end-to-end setup that achieves results comparable to the top-performing system, despite using a simpler design. These findings point to key challenges in building robust end-to-end systems and offer insight into design choices that improve performance in historical document writer identification.
Paper Structure (30 sections, 8 equations, 4 figures, 4 tables)

This paper contains 30 sections, 8 equations, 4 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Image Processing Approach
  4. Deep Learning Approach
  5. Methods
  6. Pre-processing
  7. Scale-Invariant Feature Transform (SIFT)
  8. Historical Document Binarization
  9. Text-AOI Selection
  10. Feature Extraction Model
  11. Loss Calculation
  12. Triplet Loss
  13. ArcFace Loss
  14. Post-Processing
  15. Pooling Aggregation
  16. ...and 15 more sections

Figures (4)

  • Figure 1: The scan of a historical document image may contain noise that obstructs the writing information. While this might not be a problem for manual examination by an expert, it is a difficult challenge for automatic HWI, which needs to be addressed during pre-processing. The sample is taken from the ICDAR2019 HisIR datasetchristlein2019icdar.
  • Figure 2: Sample patches taken from the historical document scan (left) using SIFT keypoint as anchor (right). With SIFT not understanding the general writing pattern, it may result in incorrect keypoint selections when used in an image with heavy-textured noise. The sample is taken from the ICDAR2019 HisIR datasetchristlein2019icdar.
  • Figure 3: The process of transforming a historical document scan with Text-AOI selection, showing the transformation from the original image (left), into binarization output (middle), ending with the Text-AOI image (right). The data is sampled from ICDAR2019 HisIR dataset christlein2019icdar.
  • Figure 4: Training and validation loss comparison between different backbone setups on ICDAR2013-WI dataset. We can see that the SwinV2-Base model has better training and validation trends compared to the other models, with the version with L2 regularization and cosine distance having a more constant loss reduction.