Handwritten Text Line Segmentation Research Articles

An overview of statistical and neural-based line segmentation methods for offline handwriting recognition task

The object of the research is the line segmentation task. To recognize the handwritten text from the documents in image format offline handwriting recognition technology is used. The text recognizer module accepts input as separate lines, so one of the important preprocessing steps is the detection and splitting of all handwritten text into distinct lines. In this paper, the handwritten text line segmentation task, its requirements, problems, and challenges are examined. Two main approaches for this task that are used in modern recognition systems are reviewed. These approaches are statistical projection-based methods and neural-based methods. Multiple works and research papers for each type of approach are reviewed analyzing their strengths and weaknesses considering the described tasks, constraints, and input data peculiarities. Overall acquired results are formed in a single table for comparison. Based on the latest works that utilize deep neural networks the new possibilities of using these methods in recognition systems are described that were unavailable with traditional statistical segmentation approaches. The constructive conclusions are made based on the review, describing the main pros and cons of these two approaches for the line segmentation task. These results can be further used for the correct selection of suitable methods in handwriting recognition systems to improve their performance and quality, and for further research in this area.

A generalized line segmentation method for multi-script handwritten text documents

Handwritten document image segmentation into text-lines is a crucial stage towards unconstrained handwritten document recognition. In the context of Indian subcontinent various scripts are used for communication where a system for multi-script handwritten text line segmentation is very much essential. This paper presents a multi-script text line segmentation algorithm based on newly developed light projection, start point detection, and boundary tracking methods. The proposed approach is capable of overcoming most of the hindrance faced by state-of-the-art methods. The experiment is performed on our proposed Bangla handwritten document image dataset WBSUBNdb_text and also on a variety of well-known public handwritten datasets namely: CMATERdb, PhDIndic_11, KHATT, HIT-MW, ISI Bengali Writer Identification/Verification dataset, ICDAR 2013 segmentation contest dataset, ICDAR 2013 writer identification contest benchmark dataset, and obtained promising results.

An adaptive threshold algorithm for offline Uyghur handwritten text line segmentation

This paper presents an effective text-line segmentation algorithm and evaluates its performance on Uyghur handwritten text document images. Projection based adaptive threshold selection mechanism is implemented to detect and segment the text lines with different valued thresholds. The robustness of the proposed algorithm is admirable that experiments on 210 Uyghur handwritten document image including 2570 text lines got correct segmentation by 97.70% precision and 99.01% recall rate and outperformed the compared classic text-line segmentation algorithm on same evaluation set. Additionally, the proposed algorithm is tested on the public handwriting dataset and get 98.05% correct segmentation rate which is robust and promising.

Fully convolutional network with dilated convolutions for handwritten text line segmentation

We present a learning-based method for handwritten text line segmentation in document images. Our approach relies on a variant of deep fully convolutional networks (FCNs) with dilated convolutions. Dilated convolutions allow to never reduce the input resolution and produce a pixel-level labeling. The FCN is trained to identify X-height labeling as text line representation, which has many advantages for text recognition. We show that our approach outperforms the most popular variants of FCN, based on deconvolution or unpooling layers, on a public dataset. We also provide results investigating various settings, and we conclude with a comparison of our model with recent approaches defined as part of the cBAD ( https://scriptnet.iit.demokritos.gr/competitions/5/ ) international competition, leading us to a 91.3% F-measure.

Unsupervised multi-language handwritten text line segmentation

Unsupervised multi-language handwritten text line segmentation

Southeast Asian palm leaf manuscript images: a review of handwritten text line segmentation methods and new challenges

Due to their specific characteristics, palm leaf manuscripts provide new challenges for text line segmentation tasks in document analysis. We investigated the performance of six text line segmentation methods by conducting comparative experimental studies for the collection of palm leaf manuscript images. The image corpus used in this study comes from the sample images of palm leaf manuscripts of three different Southeast Asian scripts: Balinese script from Bali and Sundanese script from West Java, both from Indonesia, and Khmer script from Cambodia. For the experiments, four text line segmentation methods that work on binary images are tested: the adaptive partial projection line segmentation approach, the A* path planning approach, the shredding method, and our proposed energy function for shredding method. Two other methods that can be directly applied on grayscale images are also investigated: the adaptive local connectivity map method and the seam carving-based method. The evaluation criteria and tool provided by ICDAR2013 Handwriting Segmentation Contest were used in this experiment.

A Four-Tier Annotated Urdu Handwritten Text Image Dataset for Multidisciplinary Research on Urdu Script

This article introduces a large handwritten text document image corpus dataset for Urdu script named CALAM (Cursive And Language Adaptive Methodologies). The database contains unconstrained handwritten sentences along with their structural annotations for the offline handwritten text images with their XML representation. Urdu is the fourth most frequently used language in the world, but due to its complex cursive writing script and low resources, it is still a thrust area for document image analysis. Here, a unified approach is applied in the development of an Urdu corpus by collecting printed texts, handwritten texts, and demographic information of writers on a single form. CALAM contains 1,200 handwritten text images, 3,043 lines, 46,664 words, and 101,181 ligatures. For capturing maximum variance among the words and handwritten styles, data collection is distributed among six categories and 14 subcategories. Handwritten forms were filled out by 725 different writers belonging to different geographical regions, ages, and genders with diverse educational backgrounds. A structure has been designed to annotate handwritten Urdu script images at line, word, and ligature levels with an XML standard to provide a ground truth of each image at different levels of annotation. This corpus would be very useful for linguistic research in benchmarking and providing a testbed for evaluation of handwritten text recognition techniques for Urdu script, signature verification, writer identification, digital forensics, classification of printed and handwritten text, categorization of texts as per use, and so on. The experimental results of some recently developed handwritten text line segmentation techniques experimented on the proposed dataset are also presented in the article for asserting its viability and usability.

Unconstrained Handwritten Text Line Segmentation: An Improved Technique

Unconstrained Handwritten Text Line Segmentation: An Improved Technique

A Linked List Approach for Handwritten Textline Segmentation

A Linked List Approach for Handwritten Textline Segmentation

A new scheme for unconstrained handwritten text-line segmentation

Variations in inter-line gaps and skewed or curled text-lines are some of the challenging issues in segmentation of handwritten text-lines. Moreover, overlapping and touching text-lines that frequently appear in unconstrained handwritten text documents significantly increase segmentation complexities. In this paper, we propose a novel approach for unconstrained handwritten text-line segmentation. A new painting technique is employed to smear the foreground portion of the document image. The painting technique enhances the separability between the foreground and background portions enabling easy detection of text-lines. A dilation operation is employed on the foreground portion of the painted image to obtain a single component for each text-line. Thinning of the background portion of the dilated image and subsequently some trimming operations are performed to obtain a number of separating lines, called candidate line separators. By using the starting and ending points of the candidate line separators and analyzing the distances among them, related candidate line separators are connected to obtain segmented text-lines. Furthermore, the problems of overlapping and touching components are addressed using some novel techniques. We tested the proposed scheme on text-pages of English, French, German, Greek, Persian, Oriya and Bangla and remarkable results were obtained.