Graph Clustering-Based Ensemble Method for Handwritten Text Line Segmentation

Text line segmentation in unconstrained handwritten documents remains a challenge because handwritten text lines are multi-skewed and not obviously separated. This paper presents a new approach based on the variational Bayes (VB) framework for text line segmentation. Viewing the document image as a mixture density model, with each text line approximated by a Gaussian component, the VB method can automatically determine the number of components. We extend the VB method such that it can both eliminate and split components and control the orientation of text line lines. Experiments on Chinese handwritten documents demonstrated the effectiveness of the approach.

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 This work is the first to directly compare Mask-RCNN- and U-Net-based networks on text segmentation of historical documents, showing the superiority of the former over the latter.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.

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.

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.

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.

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.

Text line segmentation in handwritten documents is an important step in many high level processing such as handwritten document enhancement and text recognition. In this paper we describe a novel approach of text line segmentation based on tracking. In this sense, we consider each connected component in the image as a moving object in its respective line and find its best match given its history motion, i.e. the closest connected component that lie in its trajectory. Direction of motion gives direction of handwritten text and is the output of our tracking algorithm. We apply our approach to images of ICDAR 2013 handwritten segmentation contest and report an overall detection rate of $$86.51\%$$ .

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.

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.

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.

Segmentation is a very critical stage in the character recognition process as the performance of any character recognition system depends heavily on the accuracy of segmentation. Although segmentation is a well-researched area, segmentation of handwritten text is still difficult owing to several factors like skewed and overlapping lines, the presence of touching, broken and degraded characters, and variations in writing styles. Therefore, researchers in this area are working continuously to develop new techniques for the efficient segmentation and recognition of characters. In the character recognition process, segmentation can be implemented at the line, word, and character level. Text line segmentation is the first step in the text/character recognition process. The line segmentation methods used in the character recognition of handwritten documents are presented in this paper. The various levels of segmentation which include line, word, and character segmentation are discussed with a focus on line segmentation.KeywordsSegmentationOCRPattern recognitionLine segmentationWord segmentationCharacter segmentationTouching lines segmentationOverlapping lines segmentation

In this work, we propose a character segmentation system from ancient palm leaf manuscripts written in ancient Arya Ezhuthu(which was popularized as script to write Malayalam from 17 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> century). The automatic recognition of handwritten text from digital images of palm leaves dating back to 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> century BC. This can enable context based searching in large volumes of digital document. The subject about document image understanding is to extract and classify individual data meaningfully from palm leaf manuscript. We propose an automatic approach to detecting and identifying the characters. The proposed approach can improve the readability of the document. Text line segmentation is then applied and the characters are extracted.

In this paper, we propose a new model for text line segmentation and classification, which consists of convolutional and two-layer bi-directional long short-term memory (BiLSTM) networks. Trained on the synthetic text dataset, it performs excellently when predicting the real data. Without labelling every line on the real data, a generalized standard for evaluating the accuracy is proposed. We also propose a simplified IoU loss to improve the execution speed greatly. In the experiments, it achieves 98.1% line segmentation accuracy and 99.5% classification accuracy on the English fiction Pride and Prejudice by Jane Austen, and achieves 98.5% line segmentation accuracy and 99.7% classification accuracy on the The Secret Of Plato's Atlantis by John Arundell, outperforming the traditional methods. Furthermore, for 1024 × 724 input samples, it gets 2.95 FPS speed when using a Tesla K80 GPU. Index Terms—Text line segmentation, Text classification, Synthetic text, BiLSTM, Convolutional network.