Document Page Images Research Articles

An Unsupervised and Robust Line and Word Segmentation Method for Handwritten and Degraded Printed Document

Segmentation of text lines and words in an unconstrained handwritten or a machine-printed degraded document is a challenging document analysis problem due to the heterogeneity in the document structure. Often there is un-even skew between the lines and also broken words in a document. In this article, the contribution lies in segmentation of a document page image into lines and words. We have proposed an unsupervised, robust, and simple statistical method to segment a document image that is either handwritten or machine-printed (degraded or otherwise). In our proposed method, the segmentation is treated as a two-class classification problem. The classification is done by considering the distribution of gap size (between lines and between words) in a binary page image. Our method is very simple and easy to implement. Other than the binarization of the input image, no pre-processing is necessary. There is no need of high computational resources. The proposed method is unsupervised in the sense that no annotated document page images are necessary. Thus, the issue of a training database does not arise. In fact, given a document page image, the parameters that are needed for segmentation of text lines and words are learned in an unsupervised manner. We have applied our proposed method on several popular publicly available handwritten and machine-printed datasets (ISIDDI, IAM-Hist, IAM, PBOK) of different Indian and other languages containing different fonts. Several experimental results are presented to show the effectiveness and robustness of our method. We have experimented on ICDAR-2013 handwriting segmentation contest dataset and our method outperforms the winning method. In addition to this, we have suggested a quantitative measure to compute the level of degradation of a document page image.

Script Identification for Printed and Handwritten Indian Documents: An Empirical Study of Different Feature Classifier Combinations

The identification of script in a document page image is the first step for an OCR system processing multi-script documents. In this multilingual/multiscript world, document processing systems relying on the OCR that need human involvement to select the appropriate OCR package is definitely undesirable and inefficient. The development of robust and efficient methods for automatic script identification of a document is a subject of major importance for automatic document processing in a multilingual/multiscript environment. Thus, the basic objective is to come up with some intuitive methods having straightforward implementation without compromising with efficiency. The aim of this work is to evaluate state-of-the-art feature extraction and classification techniques in the field of automatic script identification of printed and handwritten documents and to propose the best combination for the same.

Automatic Abstraction of Combinational Logic Circuit from Scanned Document Page Images

Information extraction from scanned document page images is an important issue in image analysis. The main objectives of this work are: vectorization of image of the digital logic-gate circuits as graph, and automatic generation of Boolean expression. We have employed a novel method for circuit component separation using morphological operators. Connecting wires (in the form of poly lines in the image) lead to adjacency matrix describing directed interconnection between logic gates. Logic gate symbols are recognized by support vector machine (SVM) based on the features obtained by deep convolutional neural network (DCNN). Finally, we exploit this abstract representation of digital logic circuit as a graph to determine the Boolean expression. The approach is tested on a dataset developed by us and the results are encouraging.

Development of a Two-Stage Segmentation-Based Word Searching Method for Handwritten Document Images

Abstract Word searching or keyword spotting is an important research problem in the domain of document image processing. The solution to the said problem for handwritten documents is more challenging than for printed ones. In this work, a two-stage word searching schema is introduced. In the first stage, all the irrelevant words with respect to a search word are filtered out from the document page image. This is carried out using a zonal feature vector, called pre-selection feature vector, along with a rule-based binary classification method. In the next step, a holistic word recognition paradigm is used to confirm a pre-selected word as search word. To accomplish this, a modified histogram of oriented gradients-based feature descriptor is combined with a topological feature vector. This method is experimented on a QUWI English database, which is freely available through the International Conference on Document Analysis and Recognition 2015 competition entitled “Writer Identification and Gender Classification.” This technique not only provides good retrieval performance in terms of recall, precision, and F-measure scores, but it also outperforms some state-of-the-art methods.

A segmentation-free word spotting method for historical printed documents

In this paper, a two-step segmentation-free word spotting method for historical printed documents is presented. The first step involves a minimum distance matching between a query keyword image and a document page image using keypoint correspondences. In the second step of the method, the matched keypoints on the document image serve as indicators for creating candidate image areas. The query keyword image is matched against the candidate image areas in order to properly estimate the bounding boxes of the detected word instances. The method is evaluated using two datasets of different languages and is compared against segmentation-free state-of-the-art methods. The experimental results show that the proposed method outperforms significantly the competitive approaches.

A Unified Algorithm for Identification of Various Tabular Structures from Document Images

This paper presents a unified algorithm for segmentation and identification of various tabular structures from document page images. Such tabular structures include conventional tables and displayed math-zones, as well as Table of Contents TOC and Index pages. After analyzing the page composition, the algorithm initially classifies the input set of document pages into tabular and non-tabular pages. A tabular page contains at least one of the tabular structures, whereas a non-tabular page does not contain any. The approach is unified in the sense that it is able to identify all tabular structures from a tabular page, which leads to a considerable simplification of document image segmentation in a novel manner. Such unification also results in speeding up the segmentation process, because the existing methodologies produce time-consuming solutions for treating different tabular structures as separate physical entities. Distinguishing features of different kinds of tabular structures have been used in stages in order to ensure the simplicity and efficiency of the algorithm and demonstrated by exhaustive experimental results.

Page segmentation for document image analysis using a neural network

In this paper we present a method for segmenting document page images into text and nontext regions. The underlying assumption made by this approach is that the two regions can be viewed as different textures. We do not use any a priori knowledge of the document format. A convolution-based method is used to generate the texture feature im- ages. The coefficients of the convolution masks are obtained using a single-layer artificial neural network that generates eigenvectors of the correlation matrix of the input data. The coefficients of these masks have been ''learned'' from examples of the document images and have a po- tential of being considerably more powerful than masks with preset co- efficients. A thresholding scheme based on a measure of entropy is used to segment the feature images into the homogeneous regions. © 1996 Society of Photo-Optical Instrumentation Engineers. Subject terms: document page segmentation; texture analysis; artificial neural networks; convolution; entropy thresholding.