Document Segmentation Research Articles

Document Layout Analysis

Document layout analysis (DLA) is a preprocessing step of document understanding systems. It is responsible for detecting and annotating the physical structure of documents. DLA has several important applications such as document retrieval, content categorization, text recognition, and the like. The objective of DLA is to ease the subsequent analysis/recognition phases by identifying the document-homogeneous blocks and by determining their relationships. The DLA pipeline consists of several phases that could vary among DLA methods, depending on the documents’ layouts and final analysis objectives. In this regard, a universal DLA algorithm that fits all types of document-layouts or that satisfies all analysis objectives has not been developed, yet. In this survey paper, we present a critical study of different document layout analysis techniques. The study highlights the motivational reasons for pursuing DLA and discusses comprehensively the different phases of the DLA algorithms based on a general framework that is formed as an outcome of reviewing the research in the field. The DLA framework consists of preprocessing, layout analysis strategies, post-processing, and performance evaluation phases. Overall, the article delivers an essential baseline for pursuing further research in document layout analysis.

Research on Tibetan Document Segmentation Based on GMM+K-means Algorithm

Research on Tibetan Document Segmentation Based on GMM+K-means Algorithm

Effective Method for Segmentation of Arabic Manuscript Documents

Effective Method for Segmentation of Arabic Manuscript Documents

Bank Form Classification using Document Layout Analysis and Image Processing Techniques

Every day thousands of forms are filled out and submitted across the world, in banks, post offices, government organizations, educational institutions etc. These include electronic forms as well as physical forms. All of these forms irrespective of their origin are at some stage made digital and stored electronically to address issues of physical storage, form degradation and data accessibility. Document layout analysis is a basic step in converting document images into electronic form. This conversion is laborious and can be made more efficient (in terms of throughput and human resource) by automating most of the conversion process using document layout analysis techniques. Document classification is an important step in Office Automation, Digital Libraries, and other document image analysis applications. Physical forms require human supervision for any operations done on the form. Digitization of these forms reduces human resources, also reduces any human redundancy involved with the operation on the physical forms. This paper addresses the initial stage of this automation, namely, bank form classification and decipherment of fields. The former recognizes the type of the bank form and the latter extracts regions of useful data from the classified bank form. The proposed work aims to provide accurate bank form classification along with noise removal, skew detection and correction, finally layout analysis is carried out to extract fields like name, address, signature from the classified forms.

A Comprehensive Framework for OCR Web Services System for Arabic Calligraphy Documents

This paper describes document layout analysis web services approach for OCR systems, in case of integrate with web-based applications using SOAP and REST interfaces. The proposed solution provides accessing way to use different OCR systems. Therefore, these web services are implemented using SOAP and REST interfaces through HTTP or HTTPS requests. Consequently, different developers can communicate with each other’s without time consuming to customize code implementation, operating system barriers, and programming language conditions. The scientific scope of this paper focuses on three objectives:(1)  The document categories on which they are included in the dataset, (2) The related algorithms that are used in the level of document analysis, and (3) The Arabic document image segmentation algorithms they are used. Consequently, the connected components method is used to remove page frame in the old and calligraphy documents. Also, shadow noises in the old and historical documents are removed using the adapted sparse algorithm.This paper discusses a number of the major areas where OCR web services have been working comprehensively: in supporting document analysis and OCR service-oriented architecture computing. Using the OCR web services approaches, we are dealing with heterogeneous large scale documents with wide varying structured category. Furthermore, there could be multipage document with different languages. Accordingly, the language domain will be identified within the language script specification module.Â

Handwritten Arabic Documents Segmentation into Text Lines using Seam Carving

Inspired from human perception and common text documents characteristics based on readability constraints, an Arabic text line segmentation approach is proposed using seam carving. Taking the gray scale of the image as input data, this technique offers better results at extracting handwritten text lines without the need for the binary representation of the document image. In addition to its fast processing time, its versatility permits to process a multitude of document types, especially documents presenting low text-to-background contrast such as degraded historical manuscripts or complex writing styles like cursive handwriting. Even if our focus in this paper was on Arabic text segmentation, this method is language independent. Tests on a public database of 123 handwritten Arabic documents showed a line detection rate of 97.5% for a matching score of 90%.

The Use of Simple Cellular Automata in Image Processing

Metadata extraction from documents forms an essential part of web or desktop search systems.Similarly, digital libraries that index scholarly literature require to find and extract the title, the list of authors and other publication-related information from an article. We present a hybrid approach for metadata extraction, combining classification and clustering to extract the desired information without the need of a conventional labeled dataset for training. An important asset of the proposed method is that the resulting clustering parameters can be used in other problems, e.g. document layout analysis.

A robust system for document layout analysis using multilevel homogeneity structure

This paper presents a robust system for the document layout analysis.The proposed system is based on multilevel homogeneity structure (MHS).The proposed system is designed to work with many different document languages.Our system is tested on four published datasets with different document languages.The proposed system (MHS) won the RDCL-2015 competition (ICDAR2015). One of the difficulties in the understanding of document images is document layout analysis, which is the first step in document image modeling. In this paper, a robust system for which a multilevel-homogeneity structure is used in accordance with a hybrid methodology is proposed to deal with this problem. Our system consists of the following three main stages: classification, segmentation, and refinement and labeling. Different from other page segmentation methods, the proposed system includes an efficient algorithm to detect table regions in document images. Besides, to create an effective application, the proposed system is designed to work with a variety of document languages. The proposed method was tested with the ICDAR2015 competition (RDCL-2015) and three other published datasets in different languages. The results of these tests show that the accuracy of proposed system is superior to the previous methods.

Arabic document layout analysis

Document layout analysis is a key step in the process of converting document images into text. Arabic language script is cursive and written in different styles which cause some challenges in the analysis of Arabic text documents. In this paper, we introduce an approach for Arabic documents layout analysis. In that approach, the document is segmented into set of zones using morphological operations. The segmented zones are classified as text or non-text ones using a support vector machine classifier. Features used in zone classification are combination between texture-based features and connected component-based features. The textural-based feature vector size is reduced using genetic algorithm. Classified text zones are clustered, using adaptive sample set clustering algorithm, into lines. Each segmented line is segmented into words by clustering inter- and intra-spaces. The proposed system was evaluated against two other systems that represent the best available tools for the Arabic documents analysis, and evaluation results show that the proposed system works well on multi-font and multi-size documents with a variety of layouts even on some historical documents.

Segmentation of Arabic Handwritten Documents into Text Lines using Watershed Transform

A crucial task in character recognition systems is the segmentation of the document into text lines and especially if it is handwritten. When dealing with non-Latin document such as Arabic, the challenge becomes greater since in addition to the variability of writing, the presence of diacritical points and the high number of ascender and descender characters complicates more the process of the segmentation. To remedy with this complexity and even to make this difficulty an advantage since the focus is on the Arabic language which is semi-cursive in nature, a method based on the Watershed Transform technique is proposed. Tested on «Handwritten Arabic Proximity Datasets» a segmentation rate of 93% for a 95% of matching score is achieved.

Binarization of Degraded Documents using Local Thresholding based on Moving Averages

Objectives: Image Binarization is the method which converts coloured and grayscale images into black and white images, black as foreground and white as background. This paper presents the new technique for binarization of degraded documents using moving averages. Methods/Statistical Analysis: Binarization of highly degraded images is very challenging task. In the proposed technique, moving averages is calculated horizontally and vertically by setting up the value of parameters. After this, local threshold value is calculated for every pixel based on moving averages within the local rectangular block both horizontally and vertically. Findings: The proposed method has been tested with large number of degraded documents and compared with existing techniques. The proposed technique makes use of the intensity of neighbouring pixels both upward and from left to right. The proposed method produce binarization results better when compared with existing Sauvola and Niblack’s Method. Applications: The proposed method produces a bianrized image which can be used in various applications like Optical character recognition, document layout analysis.

Document segmentation and classification into musical scores and text

A new algorithm for segmenting documents into regions containing musical scores and text is proposed. Such segmentation is a required step prior to applying optical character recognition and optica...

A mixture model using Random Rotation Bounding Box to detect table region in document image

Table detection in the document image is still a challenging problem due to the variety of table structures and the complexity of document layout. In this paper, we propose a novel method for detecting table regions by using a new shape which is called Random Rotation Bounding Box. This shape is used for illustration and description of the table regions. Based on it, our system performs the following three fundamental steps to detect the table zones: classification of the text and non-text elements in the document image, detection of the ruling-line tables, and identification of the non-ruling-line tables. Different from other methods, our approach can detect most kinds of tables with high precision even when it is skewed. Besides, the proposed method is also designed to fit in the document layout analysis system. Our algorithm has been tested on the two well-known, and a commercial datasets: ICDAR2013 table competition, UNLV, and Diotek. Experimental results on these databases show that our method is more robust and efficient than previous systems.

Page segmentation using minimum homogeneity algorithm and adaptive mathematical morphology

Document layout analysis or page segmentation is the task of decomposing document images into many different regions such as texts, images, separators, and tables. It is still a challenging problem due to the variety of document layouts. In this paper, we propose a novel hybrid method, which includes three main stages to deal with this problem. In the first stage, the text and non-text elements are classified by using minimum homogeneity algorithm. This method is the combination of connected component analysis and multilevel homogeneity structure. Then, in the second stage, a new homogeneity structure is combined with an adaptive mathematical morphology in the text document to get a set of text regions. Besides, on the non-text document, further classification of non-text elements is applied to get separator regions, table regions, image regions, etc. The final stage, in refinement region and noise detection process, all regions both in the text document and non-text document are refined to eliminate noises and get the geometric layout of each region. The proposed method has been tested with the dataset of ICDAR2009 page segmentation competition and many other databases with different languages. The results of these tests showed that our proposed method achieves a higher accuracy compared to other methods. This proves the effectiveness and superiority of our method.

Document segmentation using textural features summarization and feedforward neural network

Document Segmentation is a process that aims to filter documents while identifying certain regions of interest. Generally, the regions of interest include texts, graphics (image occupied regions) and the background. This paper presents a novel top-bottom approach to perform document segmentation using texture features that are extracted from the specified/selected documents. A mask of suitable size is used to summarize textural features, and statistical parameters are captured as blocks in document images. Four textural features that are extracted from masks using the gray level co-occurrence matrix (glcm) include entropy, contrast, energy and homogeneity. Furthermore, two statistical parameters extracted from corresponding masks are the modal and median pixel values. The extracted attributes allow the classification of each mask or block as text, graphics, and background. A feedforward network is trained on the 6 extracted attributes, using documents obtained from a public database ; an error rate of 15.77 % is achieved. Furthermore, it is shown that this novel approach produces promising performance in segmenting documents and is expected to be significantly efficient for content-based information retrieval systems. Detection of duplicate documents within large databases is another potential area of application.

A Zone Classification Approach for Arabic Documents using Hybrid Features

Zone segmentation and classification is an important step in document layout analysis. It decomposes a given scanned document into zones. Zones need to be classified into text and non-text, so that only text zones are provided to a recognition engine. This eliminates garbage output resulting from sending non-text zones to the engine. This paper proposes a framework for zone segmentation and classification. Zones are segmented using morphological operation and connected component analysis. Features are then extracted from each zone for the purpose of classification into text and non-text. Features are hybrid between texture-based and connected component based features. Effective features are selected using genetic algorithm. Selected features are fed into a linear SVM classifier for zone classification. System evaluation shows that the proposed zone classification works well on multi-font and multi-size documents with a variety of layouts even on historical documents.

Table Detection from Document Image using Vertical Arrangement of Text Blocks

Table detection is a challenging problem and plays an important role in document layout analysis. In this paper, we propose an effective method to identify the table region from document images. First, the regions of interest (ROIs) are recognized as the table candidates. In each ROI, we locate text components and extract text blocks. After that, we check all text blocks to determine if they are arranged horizontally or vertically and compare the height of each text block with the average height. If the text blocks satisfy a series of rules, the ROI is regarded as a table. Experiments on the ICDAR 2013 dataset show that the results obtained are very encouraging. This proves the effectiveness and superiority of our proposed method.

Separation of Text and Non-text in Document Layout Analysis using a Recursive Filter

A separation of text and non-text elements plays an important role in document layout analysis. A number of approaches have been proposed but the quality of separation result is still limited due to the complex of the document layout. In this paper, we present an efficient method for the classification of text and non-text components in document image. It is the combination of whitespace analysis with multi-layer homogeneous regions which called recursive filter. Firstly, the input binary document is analyzed by connected components analysis and whitespace extraction. Secondly, a heuristic filter is applied to identify non-text components. After that, using statistical method, we implement the recursive filter on multi-layer homogeneous regions to identify all text and non-text elements of the binary image. Finally, all regions will be reshaped and remove noise to get the text document and non-text document. Experimental results on the ICDAR2009 page segmentation competition dataset and other datasets prove the effectiveness and superiority of proposed method.

Local Similarity based Document Layout Analysis using Improved ARLSA

In this paper, we propose an efficient document layout analysis algorithm that includes table detection. Typical methods of document layout analysis use the height and gap between words or columns. To correspond to the various styles and sizes of documents, we propose an algorithm that uses the mean value of the distance transform representing thickness and compare with components in the local area. With this algorithm, we combine a table detection algorithm using the same feature as that of the text classifier. Table candidates, separators, and big components are isolated from the image using Connected Component Analysis (CCA) and distance transform. The key idea of text classification is that the characteristics of the text parallel components that have a similar thickness and height. In order to estimate local similarity, we detect a text region using an adaptive searching window size. An improved adaptive run-length smoothing algorithm (ARLSA) was proposed to create the proper boundary of a text zone and non-text zone. Results from experiments on the ICDAR2009 page segmentation competition test set and our dataset demonstrate the superiority of our dataset through f-measure comparison with other algorithms.

Document Layout Analysis Based on Fuzzy Energy Matrix

In this paper, we describe a novel method for document layout analysis that is based on a Fuzzy Energy Matrix (FEM). A FEM is a two-dimensional matrix that contains the likelihood of text and non-text and is generated through the use of Fuzzy theory. The key idea is to define an Energy map for the document to categorize text and non-text. The proposed mechanism is designed for execution with a low-resolution document image, and hence our method has a fast processing speed. The proposed method has been tested on public ICDAR 2009 datasets to conduct a comparison against other state-of-the-art methods, and it was also tested with Korean documents. The results of the experiment indicate that this scheme achieves superior segmentation accuracy, in terms of both precision and recall, and also requires less time for computation than other state-of-the-art document image analysis methods.