A Variational Bayes Method for Handwritten Text Line Segmentation

Text line segmentation is a necessary preliminary step before most text transcription algorithms are applied. The leading deep learning networks used in this context (ARU-Net, dhSegment, and Doc-UFCN) are based on the U-Net architecture. They are efficient, but fall under the same concept, requiring a post-processing step to perform instance (e.g., text line) segmentation. In the present work, we test the advantages of Mask-RCNN, which is designed to perform instance segmentation directly. This work is the first to directly compare Mask-RCNN- and U-Net-based networks on text segmentation of historical documents, showing the superiority of the former over the latter. Three studies were conducted, one comparing these networks on different historical databases, another comparing Mask-RCNN with Doc-UFCN on a private historical database, and a third comparing the handwritten text recognition (HTR) performance of the tested networks. The results showed that Mask-RCNN outperformed ARU-Net, dhSegment, and Doc-UFCN using relevant line segmentation metrics, that performance evaluation should not focus on the raw masks generated by the networks, that a light mask processing is an efficient and simple solution to improve evaluation, and that Mask-RCNN leads to better HTR performance.

Despite recent advances in the field of supervised deep learning for text line segmentation, unsupervised deep learning solutions are beginning to gain popularity. In this paper, we present an unsupervised deep learning method that embeds document image patches to a compact Euclidean space where distances correspond to a coarse text line pattern similarity. Once this space has been produced, text line segmentation can be easily implemented using standard techniques with the embedded feature vectors. To train the model, we extract random pairs of document image patches with the assumption that neighbour patches contain a similar coarse trend of text lines, whereas if one of them is rotated, they contain different coarse trends of text lines. Doing well on this task requires the model to learn to recognize the text lines and their salient parts. The benefit of our approach is zero manual labelling effort. We evaluate the method qualitatively and quantitatively on several variants of text line segmentation datasets to demonstrate its effectivity.

We investigate how well a variational Bayes (VB) method performs as an approximation for the demanding computations typically required in Gaussian process regression. We study a technique using so-called emph{inducing variables} to drastically reduce the computational load. The key question is: can we make equally good estimates with less computational effort? We show that this is indeed possible, provided that one carefully chooses emph{how many} and emph{which} inducing variables. If too few are chosen, the resulting model becomes too `smooth', leading to inaccurate estimates and an overestimation of uncertainty. However, with a sufficient number of well-chosen inducing variables, the VB method can be just as accurate as the original, much slower approach. Faster computation with retained accuracy is feasible, as long as the number of inducing variables is tailored to the complexity of the true underlying function. Besides providing a point estimate, we also want to know how certain we can be about it. A key advantage of the Bayesian approach is that it allows for emph{uncertainty quantification}. We analyse VB methods based on spectral features and examine how well these methods capture uncertainty. Surprisingly, these methods can yield reliable uncertainty estimates under certain conditions, which is not always true for other VB approaches. Thus, this method can offer not only accurate predictions but also trustworthy uncertainty estimates, provided the priors are well-matched to the underlying data. Finally, we address a practical challenge: how do you choose the right settings (hyperparameters) for a model when you don’t know how complex the real world is? We consider an emph{adaptive} VB method that adjusts itself to the complexity of the data without requiring prior knowledge. We combine a hierarchical Bayesian model with a variational approach, and demonstrate both theoretically and through experiments that this works well. The result is a fast method that automatically adapts to various types of underlying functions while still achieving optimal performance. Self-tuning VB methods are therefore possible and deliver strong results without requiring prior knowledge of the complexity of the data-generating mechanism.

Segmentation of text lines is one of the important steps in the Optical Character Recognition system. Text Line Segmentation is pre-processing step of word and character segmentation. Text Line Segmentation can be viewed simple for printing documents which contains distinct spaces between the lines. And it is more complex for the documents where text lines are overlap, touch, curvilinear and variation of space between text lines like in Telugu scripts and skewed documents. The main objective of this project is to investigate different text line segmentation algorithms like Projection Profiles, Run length smearing and Adaptive Run length smearing on low quality documents. These methods are experimented and compare their accuracy and results.

Text line detection is crucial for any application associated with Automatic Text Recognition or Keyword Spotting. Modern algorithms perform good on well-established datasets since they either comprise clean data or simple/homogeneous page layouts. We have collected and annotated 2036 archival document images from different locations and time periods. The dataset contains varying page layouts and degradations that challenge text line segmentation methods. Well established text line segmentation evaluation schemes such as the Detection Rate or Recognition Accuracy demand for binarized data that is annotated on a pixel level. Producing ground truth by these means is laborious and not needed to determine a method's quality. In this paper we propose a new evaluation scheme that is based on baselines. The proposed scheme has no need for binarization and it can handle skewed as well as rotated text lines. The ICDAR 2017 Competition on Baseline Detection and the ICDAR 2017 Competition on Layout Analysis for Challenging Medieval Manuscripts used this evaluation scheme. Finally, we present results achieved by a recently published text line detection algorithm.

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.

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.

Due to the presence of high volume of noise in text documents, it becomes very difficult to achieve high accuracy while performing text line segmentation. Hence, approaches which are dependent on the performance of the line segmentation stage, also suffers. Also due to the variability of noise patterns, the text contents get distorted and sometimes it results in broken strokes of a character. Thus, character level recognition becomes a challenging task for noisy documents. Our proposed method is aimed to address the aforesaid challenges. Since, we are considering the input image to be of noisy kind, a convolutional neural network (CNN) architecture is introduced to initially identify whether the input image contains noise or not. We consider this as a two-class problem and try to identify the respective classes, i.e. noisy or clean. Then, we apply the proposed piecewise projection profile feature and an adaptive region growing based two-stage algorithmic approach which initially identifies the text line upper and lower boundaries in a noisy text document image and then try to regroup the broken strokes of a character to enhance the character recognition accuracy. The proposed method has been tested on a large size dataset containing noisy documents and also different quality measures are computed to establish the effectiveness of the proposed method. From the measures, it can be noted that the text line segmentation accuracy is comparable to other similar state-of-the-art results.

Handwritten text line segmentation on real-world data presents significant challenges that cannot be overcome by any single technique. Given the diversity of approaches and the recent advances in ensemble-based combination for pattern recognition problems, it is possible to improve the segmentation performance by combining the outputs from different line finding methods. In this paper, we propose a novel graph clustering-based approach to combine the output of an ensemble of text line segmentation algorithms. A weighted undirected graph is constructed with nodes corresponding to connected components and edge connecting pairs of connected components. Text line segmentation is then posed as the problem of minimum cost partitioning of the nodes in the graph such that each cluster corresponds to a unique line in the document image. Experimental results on a challenging Arabic field dataset using the ensemble method shows a relative gain of 18% in the F1 score over the best individual method within the ensemble.

Text line segmentation (TLS) continues to be an unsolved problem in the field of document analysis, and this is especially true for lines of handwritten characters. In recent years, however, this challenging problem has seen a surge of interest. In this paper we introduce our work on a new robust framework for solving the TLS problem via a trial-and-error method within a divide-and-conquer paradigm. Specifically, we propose a set of candidate solutions from constrained optimal Markov line descriptors (COMLD) and then predict a candidate class using a classifier learned from training data. Our new framework will treat a multiple-line candidate as a new and independent TLS problem, and it will re-estimate method parameters to achieve better adaptations and thus better solutions. Our extensive experimental results indicate that this framework 1) reaches or outperforms state-of-the-art TLS solutions on various datasets; 2) is robust against various image variations including, but not limited to, rotation, noise, image, resolution, and text line spaces; and 3) is trainable for a new language/data and is self-adaptive to new testing data. This evidence suggests that our methodology is a highly promising solution to the TLS problem.

This paper presents a new Bayesian-based method of unconstrained handwritten offline Chinese text line recognition. In this method, a sample of a real character or non-character in realistic handwritten text lines is jointly recognized by a traditional isolated character recognizer and a character verifier, which requires just a moderate number of handwritten text lines for training. To improve its ability to distinguish between real characters and non-characters, the isolated character recognizer is negatively trained using a linear discriminant analysis (LDA)-based strategy, which employs the outputs of a traditional MQDF classifier and the LDA transform to re-compute the posterior probability of isolated character recognition. In tests with 383 text lines in HIT-MW database, the proposed method achieved the character-level recognition rates of 71.37% without any language model, and 80.15% with a bi-gram language model, respectively. These promising results have shown the effectiveness of the proposed method for unconstrained handwritten offline Chinese text line recognition.

Text extraction is an important phase in document recognition systems. In order to segment text from a page document it is necessary to detect all the possible manuscript text regions. In this article we propose an efficient algorithm to segment handwritten text lines. The text line algorithm uses a morphological operator to obtain the features of the images. Following, a sequence of histogram projection and recovery is proposed to obtain the line segmented region of the text. First, an Y histogram projection is performed which results in the text lines positions. To divide the lines in different regions a threshold is applied. After that, another threshold is used to eliminate false lines. These procedures, however, cause some loss on the text line area. So, a recovery method is proposed to minimize this effect. In order to detect the extreme positions of the text in the horizontal direction, an X histogram projection is applied. Then, as in the Y direction, another threshold is used to eliminate false words. Finally, in order to optimize the area of the manuscript text line, a text selection is carried out. Experimental results using the IAM-database showed that this new approach is robust, fast and produces very good score rates.

In this paper, we present our new method for the segmentation of handwritten text pages into lines, which has been submitted to ICDAR'2013 handwritten segmentation competition. This method is based on two levels of perception of the image: a rough perception based on a blurred image, and a precise perception based on the presence of connected components. The combination of those two levels of perception enables to deal with the difficulties of handwritten text segmentation: curvature, irregular slope and overlapping strokes. Thus, the analysis of the blurred image is efficient in images with high density of text, whereas the use of connected components enables to connect the text lines in the pages with low text density. The combination of those two kinds of data is implemented with a grammatical description, which enables to externalize the knowledge linked to the page model. The page model contains a strategy of analysis that can be associated to an applicative goal. Indeed, the text line segmentation is linked to the kind of data that is analysed: homogeneous text pages, separated text blocks or unconstrained text. This method obtained a recognition rate of more than 98% on last ICDAR'2013 competition.

Text line segmentation is an important preprocessing work for Dongba document analysis. It is the basis of analysis and collection work such as character segmentation, Dongba pictograph extraction, Dongba pictograph translation and annotation segmentation, and sample collection. Combining with the structural characteristics of Dongba hieroglyphic documents, we give a text line segmentation algorithm which is suitable for Dongba hieroglyphic documents. The algorithm combines K-means clustering on the basis of projection segmentation to realize the automatic determination of the height range of a single text line. At the same time, it also realizes the automatic segmentation of too large or too small text blocks. This will lay the foundation for the automatic collection of Dongba pictographs and the completion of further research on Dongba documents.

Tremendous growth in digital technology introduced new magnitudes to the digital world also raised various challenges and opened new area for researchers. Text line segmentation of historical handwritten documents became mystery for many researchers. Degradation, presence of noise, fluctuation in illumination, multiple skews, overlapping lines, touching lines, curved lines, slanting lines, uneven alignments, additional modifiers, embellishments and presence of decorators motivated researchers to develop suitable segmentation algorithm for historical documents. MODI was an ancient script of India and vanished from usual transactions, available only in old historical documents with cursive and complex type of nature. Principal interest of this work was to address various hurdles occurred during text line segmentation and propose robust method for text line segmentation from historical handwritten MODI documents. According to best of our knowledge this was the first attempt taken towards segmentation and document analysis of historical handwritten MODI documents.