Text Line Extraction Research Articles

A novel method for straightening curved text-lines in stylistic documents

Stylistic text can be found on sign boards, street and organizations boards and logos, bulletin boards, announcements, advertisements, dangerous goods plates, warning notices, etc. In stylistic text images, text-lines within an image may have different orientations such as curved in shape or not be parallel to each other. As a result, extraction and subsequent recognition of individual text-lines and words in such images is a difficult task. In this paper, we propose a novel scheme for straightening of curved text-lines using the concept of dilation, flood-fill, robust thinning, and B-spline curve-based fitting. In the proposed scheme, at first, dilation is applied on individual text-lines to cover the area within a certain boundary. Next, thinning is applied to get the path of the text, approximate the path using the B-spline, find the angle between the normal at a point on the curve and the vertical line, and finally visit each point on the text and rotate by their corresponding angles. The proposed methodology is tested on variety of text images containing text-lines in Devanagari, English, and Chinese scripts which is evaluated on the basis of visual perception and the mean square error (MSE) calculation. MSE is calculated by line fitting applied on input and output images. On the basis of evaluation results obtained in our experiments, the proposed method is promising.

Implementation of Real Time Retrieval of Text from Camera/Scanned Document Image using FPGA

For the few decades, the work on computer captured/scanned-skewed document image is a major challenge for the researchers in the field of OCR. Imaging the old historic or bulky book causes skewness and distortion on the image. The proposed metric retrieval method helps in preprocessing, skeletonization, skew detection, skew correction and recognition using horizontal and vertical text line extraction of the image. Text line detection depends on the goal of digitization. Digitization is helpful in museum and library where old historic and damaged manuscripts are imaged. In the real time the image is captured using the camera fitted in FPGA kit (cyclone II). The captured image is preprocessed with the verilog code which is converted from MATLAB code using simulink HDL coder.

Extraction of Text Lines from Handwritten Documents Using Piecewise Water Flow Technique

AbstractA novel piecewise water flow technique for text line extraction from multi-skewed document images of handwritten text of different scripts is presented here. The basic water flow technique assumes that the hypothetical water flows from both left and right sides of the image frame. This flow of water fills up the gaps between consecutive objects (texts) but faces obstruction if any object lies in the path of the flow. All unwetted regions in the document image are then labeled distinctly to extract the text lines. However, the technique fails when two neighboring text lines touch each other, as water gets obstructed by the touching segment(s). To get rid of this difficulty, we have modified the basic water flow technique by iteratively applying the same over the vertically segmented document images. The main purpose of this vertical segmentation is to localize the text line segment(s) where two text lines get joined. These segments are then horizontally fragmented, and each fragment is placed suitably to the text line in which it actually belongs to. This way, the probable data loss during isolation of the touching text line segment is minimized. Both the techniques (current and basic ones) have been tested on three different databases, viz., CMATERdb 1.1.1, CMATERdb 1.1.2, and ICDAR2009 handwritten segmentation contest pages, respectively. The test results show that the present technique outperforms the basic one for all three databases.

Text line extraction for historical document images

In this paper we present a language independent global method for automatic text line extraction. The proposed approach computes an energy map of a text image and determines the seams that pass across and between text lines. In this work we have developed two algorithms along this novel idea, one for binary images and the other for grayscale images. The first algorithm works on binary document images and assumes it is possible to extract the components along text lines. The seam passes on the middle and along the text line, l, and marks the components that make the letters and words of l. It then assigns the unmarked component to the closest text line. The second algorithm works directly on grayscale document images. It computes the distance transform directly from the grayscale images and generates two types of seams: medial seams and separating seams. The medial seams determine the text lines and the separating seams define the upper and lower boundaries of these text lines. Moreover, we present a new benchmark dataset of historical document images with various types of challenges. The dataset contains a groundtruth for text line extraction and it contains samples with different languages such as: Arabic, English and Spanish. A binary dataset is used to test the binary algorithm. We performed various experimental results using our two algorithms on the mentioned datasets and report segmentation accuracy. We also compare our algorithms with the state-of-the-art text line segmentation methods.

A Combined Algorithm for Layout Analysis of Arabic Document Images and Text Lines Extraction

Text and not -text segmentation and text line extraction from document images are the most challenging problems of information indexing of Arabic document images such as books, technical articles, business letters and faxes in order to successfully process them in systems such as OCR. Researches on Arabic language related to documents digitization have been focusing on word and handwriting recognition. Few approaches have been proposed for layout analysis for Arabic scanned/captured documents. In this paper we present a page segmentation method that deals with the complexity of the Arabic language characteristics and fonts using the combination between two algorithms. The first method is the Run length Smoothing. The second method is the Connected Component Labeling algorithm for text and non-text classification using SVM. The combination of the two methods is based on Anding and Oring operations between the outputs of the two methods based on certain conditions. Then, dynamic horizontal projection based on dynamic updating of the threshold to commensurate with the noise associated with different documents and in between text lines. The performance evaluation is performed using manually generated ground truth representations from a dataset of Arabic document images captured using cameras and a hardware built for this purpose. Evaluation and experimental results demonstrate that the proposed text extraction method is independent from different document size, text size, font, shape, and is robust to Arabic document segmentation and text lines extraction. General Terms Image processing, Pattern Recognition.

A general approach for multi-oriented text line extraction of handwritten documents

The multi-orientation occurs frequently in ancient handwritten documents, where the writers try to update a document by adding some annotations in the margins. Due to the margin narrowness, this gives rise to lines in different directions and orientations. Document recognition needs to find the lines everywhere they are written whatever their orientation. This is why we propose in this paper a new approach allowing us to extract the multi-oriented lines in scanned documents. Because of the multi-orientation of lines and their dispersion in the page, we use an image meshing allowing us to progressively and locally determine the lines. Once the meshing is established, the orientation is determined using the Wigner–Ville distribution on the projection histogram profile. This local orientation is then enlarged to limit the orientation in the neighborhood. Afterward, the text lines are extracted locally in each zone basing on the follow-up of the orientation lines and the proximity of connected components. Finally, the connected components that overlap and touch in adjacent lines are separated. The morphology analysis of the terminal letters of Arabic words is here considered. The proposed approach has been experimented on 100 documents reaching an accuracy of about 98.6%.

Text-Line Extraction in Handwritten Chinese Documents Based on an Energy Minimization Framework

Text-line extraction in unconstrained handwritten documents remains a challenging problem due to nonuniform character scale, spatially varying text orientation, and the interference between text lines. In order to address these problems, we propose a new cost function that considers the interactions between text lines and the curvilinearity of each text line. Precisely, we achieve this goal by introducing normalized measures for them, which are based on an estimated line spacing. We also present an optimization method that exploits the properties of our cost function. Experimental results on a database consisting of 853 handwritten Chinese document images have shown that our method achieves a detection rate of 99.52% and an error rate of 0.32%, which outperforms conventional methods.

Offline handwritten Amharic word recognition

This paper describes two approaches for Amharic word recognition in unconstrained handwritten text using HMMs. The first approach builds word models from concatenated features of constituent characters and in the second method HMMs of constituent characters are concatenated to form word model. In both cases, the features used for training and recognition are a set of primitive strokes and their spatial relationships. The recognition system does not require segmentation of characters but requires text line detection and extraction of structural features, which is done by making use of direction field tensor. The performance of the recognition system is tested by a dataset of unconstrained handwritten documents collected from various sources, and promising results are obtained.

Adaptive Script-Independent Text Line Extraction

In this paper, an adaptive block-based text line extraction algorithm is proposed. Three global and two local parameters are defined to adapt the method to various handwritings in different languages. A document image is segmented into several overlapping blocks. The skew of each block is estimated. Text block is de-skewed by using the estimated skew angle. Text regions are detected in the de-skewed text block. A number of data points are extracted from the detected text regions in each block. These data points are used to estimate the paths of text lines. By thinning the background of the image including text line paths, text line boundaries or separators are estimated. Furthermore, an algorithm is proposed to assign to the extracted text lines the connected components which have intersections with the estimated separators. Extensive experiments on different standard datasets in various languages demonstrate that the proposed algorithm outperforms previous methods.

Combined orientation and skew detection using geometric text-line modeling

In large scale document digitization, orientation detection plays an important role, especially in the scenario of digitizing incoming mail. The heavy use of automatic document feeding scanners and moreover automatic processing of facsimiles results in many documents being scanned in the wrong orientation. These misoriented scans have to be corrected, as most subsequent processing steps assume the document to be scanned in the right orientation. Several existing methods for orientation detection use the fact that in Latin script text, ascenders are more likely to occur than descenders. In this paper, we propose a one-step skew and orientation detection method using a well-established geometric text-line model. The advantage of our method is that it combines accurate skew estimation with robust, resolution-independent orientation detection. An interesting aspect of our method is that it incorporates orientation detection into a previously published skew detection method allowing to perform orientation detection, skew estimation, and, if necessary, text-line extraction in one step. The effectiveness of our orientation detection approach is demonstrated on the UW-I dataset, and on publicly available test images from OCRopus. Our method achieves an accuracy of 99% on the UW-I dataset and 100% on test images from OCRopus.

Morphology-based text line extraction

This paper presents a morphology-based text line extraction algorithm for extracting text regions from cluttered images. First of all, the method defines a novel set of morphological operations for extracting important contrast regions as possible text line candidates. The contrast feature is robust to lighting changes and invariant against different image transformations like image scaling, translation, and skewing. In order to detect skewed text lines, a moment-based method is then used for estimating their orientations. According to the orientation, an x-projection technique can be applied to extract various text geometries from the text-analogue segments for text verification. However, due to noise, a text line region is often fragmented to different pieces of segments. Therefore, after the projection, a novel recovery algorithm is then proposed for recovering a complete text line from its pieces of segments. After that, a verification scheme is then proposed for verifying all extracted potential text lines according to their text geometries. Experimental results show that the proposed method improves the state-of-the-art work in terms of effectiveness and robustness for text line detection.

Text line extraction from multi-skewed handwritten documents

A novel text line extraction technique is presented for multi-skewed document images of handwritten English or Bengali text. It assumes that hypothetical water flows, from both left and right sides of the image frame, face obstruction from characters of text lines. The stripes of areas left unwetted on the image frame are finally labelled for extraction of text lines. The success rate of the technique, as observed experimentally, are 90.34% and 91.44% for handwritten Bengali and English document images, respectively. The work may contribute significantly for the development of applications related to optical character recognition of Bengali/English text.

Multioriented and Curved Text Lines Extraction From Indian Documents

There are printed artistic documents where text lines of a single page may not be parallel to each other. These text lines may have different orientations or the text lines may be curved shapes. For the optical character recognition (OCR) of these documents, we need to extract such lines properly. In this paper, we propose a novel scheme, mainly based on the concept of water reservoir analogy, to extract individual text lines from printed Indian documents containing multioriented and/or curve text lines. A reservoir is a metaphor to illustrate the cavity region of a character where water can be stored. In the proposed scheme, at first, connected components are labeled and identified either as isolated or touching. Next, each touching component is classified either straight type (S-type) or curve type (C-type), depending on the reservoir base-area and envelope points of the component. Based on the type (S-type or C-type) of a component two candidate points are computed from each touching component. Finally, candidate regions (neighborhoods of the candidate points) of the candidate points of each component are detected and after analyzing these candidate regions, components are grouped to get individual text lines.

An optimization methodology for document structure extraction on Latin character documents

In this paper, we give a formal definition of a document image structure representation, and formulate document image structure extraction as a partitioning problem: finding an optimal solution partitioning the set of glyphs of an input document image into a hierarchical tree structure where entities within the hierarchy at each level have similar physical properties and compatible semantic labels. We present a unified methodology that is applicable to construction of document structures at different hierarchical levels. An iterative, relaxation-like method is used to find a partitioning solution that maximizes the probability of the extracted structure. All the probabilities used in the partitioning process are estimated from an extensive training set of various kinds of measurements among the entities within the hierarchy. The offline probabilities estimated in the training then drive all decisions in the online document structure extraction. We have implemented a text line extraction algorithm using this framework.

Extracting curved text lines using local linearity of the text line

In order to enhance the ability of document analysis systems, we need a text line extraction method which can handle not only straight text lines but also text lines in various shapes. This paper proposes a new method called Extended Linear Segment Linking (ELSL for short), which is able to extract text lines in arbitrary orientations and curved text lines. We also consider the existence of both horizontally and vertically printed text lines on the same page. The new method can produce text line candidates for multiple orientations. We verify the ability of the method by some experiments as well.

Automatic identification and skew estimation of text lines in real scene images

A method for the automatic localization of text embedded in complex images is proposed. It permits to detect the spatial position and the skew of the text lines which are present in the scene and to return a binary representation of each text line. Strengths of the algorithm are independence of text skew and of presence of connected text. After a preprocessing step the input image is segmented in order to obtain a set of connected components which represent the basic objects of the algorithm. Several heuristics are proposed to characterize text objects which depend both on the geometrical features of single components and on the geometrical and spatial relations among components. According to these heuristics several components are discarded and the retained ones are grouped into text lines candidates by means of a divisive hierachical clustering procedure. In the experimental session we describe the application of the algorithm to the extraction of text lines from the images of 100 book covers. Results about skew estimation are also reported.

Text-line extraction and character recognition of document headlines with graphical designs using complementary similarity measure

A method for recognizing characters on graphical designs is proposed. A new projection feature that separates text-line regions from backgrounds, and adaptive thresholding in displacement matching are introduced. Experimental results for newspaper headlines with graphical designs show a recognition rate of 97.7 percent.

Extraction of text lines and text blocks on document images based on statistical modeling

In this article, we developed a Bayesian model to characterize text line and text block structures on document images using the text word bounding boxes. We posed the extraction problem as finding the text lines and text blocks that maximize the Bayesian probability of the text lines and text blocks given the text word bounding boxes. In particular, we derived the so-called probabilistic linear displacement model (PLDM) to model the text line structures from text word bounding boxes. We also developed an augmented PLDM model to characterize the text block structures from text line bounding boxes. By systematically gathering statistics from a large population of document images, we are able to validate our models through experiments and determine the proper model parameters. We designed and implemented an iterative algorithm that used these probabilistic models to extract the text lines and text blocks. The quantitative performances of the algorithm in terms of the rates of miss, false, correct, splitting, merging, and spurious detections of the text lines and text blocks are reported. © 1996 John Wiley & Sons, Inc.

Robust and fast text‐line extraction using local linearity of the text‐line

AbstractText region extraction is a necessary process before character recognition is done for document images. This paper describes a new algorithm, Linear Segment Linking (LSL), for text‐line extraction from document images. The algorithm groups together the piecewise linear elements in the document images, which may be assumed to be text lines, and then extracts them from the images. The algorithm requires less knowledge about document structure and is robust for distortion of the image. The primitive rectangles are introduced for the intermediate representation of image. It is easier and faster to create them than the usual circumscribing rectangles. A method of splitting the bridges between neighboring text lines is proposed. Combining the bridge splitting process with the text line extraction, the locally touching text lines will be extracted as individual ones.