Text Line Segmentation of Historical Documents: a Survey (0704.1267v1)

Abstract: There is a huge amount of historical documents in libraries and in various National Archives that have not been exploited electronically. Although automatic reading of complete pages remains, in most cases, a long-term objective, tasks such as word spotting, text/image alignment, authentication and extraction of specific fields are in use today. For all these tasks, a major step is document segmentation into text lines. Because of the low quality and the complexity of these documents (background noise, artifacts due to aging, interfering lines),automatic text line segmentation remains an open research field. The objective of this paper is to present a survey of existing methods, developed during the last decade, and dedicated to documents of historical interest.

Text Line Segmentation of Historical Documents: A Survey

The paper "Text Line Segmentation of Historical Documents: A Survey" by Laurence Likforman-Sulem, Abderrazak Zahour, and Bruno Taconet provides an exhaustive review of methodologies pertinent to the segmentation of text lines from historical documents. The primary focus of this survey is to collate and present various techniques developed over the preceding decade tailored to address the unique challenges posed by historical documents, both printed and handwritten.

Historical documents pose a significant challenge for automatic text line segmentation due to their deteriorated quality and complex structures. These documents are often plagued with background noise, aging artifacts, and overlapping line elements, which complicate the segmentation process. Segmentation, a critical preprocessing step for further document analysis tasks such as structure extraction and character recognition, remains an open field of research due to these complexities.

Characteristics and Challenges

Historical documents stand apart from modern documents due to their informal layouts and the quality depreciation over time. Physical document structures such as baselines, median lines, and separators become problematic with phenomena like overlapping and touching components, particularly in manuscripts with cursive styles or those with narrow interline spacing. External factors, including author styles, contribute to fluctuations in baselines and line orientations, further compounding segmentation difficulties.

Methodologies

The survey delineates several methodological approaches categorized as projection-based methods, smearing methods, grouping methods, methods based on the Hough transform, repulsive-attractive network methods, and stochastic methods. Each category presents unique attributes and has its strengths and limitations vis-à-vis historical document segmentation.

1. Projection-Based Methods: These methods, adapted from printed document processing, employ vertical projection profiles to identify gaps between text lines. While effective for documents with limited skewing or clean layouts, projection-based methods struggle with highly fluctuating text lines.
2. Smearing Methods: Often used for printed documents, these methods extend applicable uses to grayscale images by accumulating gradients horizontally. They provide coherent line patterns but demand precise parameter tuning for efficacy in historical texts.
3. Grouping Methods: These involve aggregating spatially proximal characters, useful for fluctuating lines. Conflict resolution mechanisms are integral to these methods, given the non-uniform character distribution in historical scripts.
4. Hough-Based Methods: Leveraging the Hough transform's ability to detect lines within images, these methods hypothesize and validate line alignments within complex documents. This technique is beneficial for multi-directional text lines as seen in historical manuscripts.
5. Repulsive-Attractive Network: Operating on grey-level images, these algorithms iteratively refine baseline estimations using attractive and repulsive forces. They are useful for baseline extraction, particularly in ethnic scripts such as ancient Ottoman.
6. Stochastic Methods: Utilizing probabilistic frameworks like the Viterbi algorithm, these methods derive non-linear paths to segment overlapping lines. Their robustness makes them adept for documents with complex overlapping structures.

Practical and Theoretical Implications

The practical implications of these methodologies are profound for digitizing and indexing extensive collections of historical texts, enabling improved accessibility and preservation. Theoretical advancements in segmentation algorithms could facilitate more accurate document processing, contributing to fields like digital archiving, paleography, and computational linguistics.

Future Directions

As the need for processing historical manuscripts increases, advancements in AI and machine learning could play significant roles. Future research may focus on adaptive algorithms that dynamically tune parameters based on document characteristics or integrate segmentation with recognition processes for iterative refinement.

Overall, this paper serves as an indispensable resource for researchers in document analysis, underscoring the complex interplay between document attributes and segmentation strategies. By leveraging this comprehensive survey, further research and improvements can propel advancements in the automated processing of historical documents.