Text line extraction is an essential preprocessing step in many handwritten document image analysis tasks. It includes detecting text lines in a document image and segmenting the regions of each detected line. Deep learning-based methods are frequently used for text line detection. However, only a limited number of methods tackle the problems of detection and segmentation together. This paper proposes a holistic method that applies Mask R-CNN for text line extraction. A Mask R-CNN model is trained to extract text lines fractions from document patches, which are further merged to form the text lines of an entire page. The presented method was evaluated on the two well-known datasets of historical documents, DIVA-HisDB and ICDAR 2015-HTR, and achieved state-of-the-art results. In addition, we introduce a new challenging dataset of Arabic historical manuscripts, VML-AHTE, where numerous diacritics are present. We show that the presented Mask R-CNN-based method can successfully segment text lines, even in such a challenging scenario.

We present a learning-free method for text line segmentation of historical handwritten document images. This method relies on automatic scale selection together with second derivative of anisotropic Gaussian filters to detect the blob lines that strike through the text lines. Detected blob lines guide an energy minimization procedure to extract the text lines. Historical handwritten documents contain noise, heterogeneous text line heights, skews and touching characters among text lines. Automatic scale selection allows for automatic adaption to the heterogeneous nature of handwritten text lines in case the character height range is correctly estimated. In the extraction phase, the method can accurately split the touching characters among the text lines. We provide results investigating various settings and compare the model with recent learning-free and learning-based methods on the cBAD competition dataset.

In historical document image processing, datasets account for a significant part of any research, and are crucial for the diversity and abundance of experimental results, which contribute to the development of new algorithms to meet the new challenge. Moreover, they are very important for benchmarking processing algorithms. Numerous publicly available document image datasets of different languages have been emerged. However, current segmentation and recognition performances are nearly saturated with respect to the present publicly available datasets. As such, collecting and labelling historical document images is a burden on historical document image processing researchers. This paper introduces a public historical document image dataset, Pinkas dataset, with new challenges to open room for improvement and identify strengths and weaknesses of available processing algorithms. It is the first dataset in medieval handwritten Hebrew and fully labeled at word, line and page level by an expert of historical Hebrew manuscripts. Pinkas dataset contributes to the diversity of benchmarking standards. In this paper we present meta features of Pinkas dataset and apply recent word spotting algorithms to analyze the room for improvement in terms of performance.

The purpose of this survey is to provide a comprehensive overview of recent advancements in text line segmentation and baseline detection techniques within the analysis of historical document images. Text line extraction is an essential step in the historical documents image analysis pipeline, as its results significantly impact the accuracy of subsequent processes, such as handwritten text recognition (HTR). Through a multi-stage procedure, we carefully selected 49 peer-reviewed studies published since 2019. Based on careful analysis of these studies, we summarize the information of the existing datasets, describe and categorize different methods, and summarize evaluation protocols. In addition, we compare the results of various methods on benchmark datasets. Finally, we highlight the gaps and suggest directions for future research. We believe that this comprehensive survey will be of great assistance to researchers working in the field of historical document image analysis, as it offers critical insights into the latest advancements and developments, providing a foundation for future research.

This work focuses on automatic gender and age prediction tasks from handwritten documents. This problem is of interest in a variety of fields, such as historical document analysis and forensic investigations. The challenge for automatic gender and age classification can be demonstrated by the relatively low performances of the existing methods. In addition, despite the success of CNN for gender classification, deep neural networks were never applied for age classification. The published works in this area mostly concentrate on English and Arabic languages. In addition to Arabic and English, this work also considers Hebrew, which was much less studied. Following the success of bilinear Convolutional Neural Network (B-CNN) for fine-grained classification, we propose a novel implementation of a B-CNN with ResNet blocks. To our knowledge, this is the first time the bilinear CNN is applied for writer demographics classification. In particular, this is the first attempt to apply a deep neural network for the age classification. We perform experiments on documents from three benchmark datasets written in three different languages and provide a thorough comparison with the results reported in the literature. B-ResNet was top-ranked in all tasks. In particular, B-ResNet outperformed other models on KHATT and QUWI datasets on gender classification.

Most of the algorithms proposed for text line detection are designed to process binary images as input. For severely degraded documents, binarization often introduces significant noise and other artifacts. In this work we present a novel method designed to detect text lines directly in gray scale images. The method consists of two stages. Potential characters are detected in the first stage. This is done by analyzing the evolution maps of connected components obtained by a sliding threshold. The detected potential characters are grouped into text lines in the second stage using sweep-line approach. The suggested method is especially powerful when applied to torn and damaged documents that other algorithms are not able to deal with.