A SURVEY ON VARIOUS APPROACHES OF TEXT EXTRACTION IN IMAGES
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paper_title: A Robust Split-and-Merge Text Segmentation Approach for Images

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In this paper we describe a robust approach to segment text from color images. The proposed approach mainly includes four steps. Firstly, a preprocessing step is utilized to enhance text blocks in images; Secondly, these image blocks are split into connected components and most of them are eliminated by a component filtering procedure; Thirdly, the left connected components are merged into several text layers, and a set of appropriate constraints are applied to find the real text layer; finally, the text layer is refined through a post-processing step to generate a binary output. Our experimental results show that the proposed approach has a good performance in character recognition rate and processing speed. Moreover, it is robust to text color, font size, as well as different styles of characters in different languages.
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paper_title: Text extraction from graphical document images using sparse representation

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A novel text extraction method from graphical document images is presented in this paper. Graphical document images containing text and graphics components are considered as two-dimensional signals by which text and graphics have different morphological characteristics. The proposed algorithm relies upon a sparse representation framework with two appropriately chosen discriminative overcomplete dictionaries, each one gives sparse representation over one type of signal and non-sparse representation over the other. Separation of text and graphics components is obtained by promoting sparse representation of input images in these two dictionaries. Some heuristic rules are used for grouping text components into text strings in post-processing steps. The proposed method overcomes the problem of touching between text and graphics. Preliminary experiments show some promising results on different types of document.
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paper_title: Textline information extraction from grayscale camera-captured document images

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Cameras offer flexible document imaging, but with uneven shading and non-planar page shape. Therefore cameracaptured documents need to go through dewarping before being processed by traditional text recognition methods. Curled textline detection is an important step of dewarping. Previous approaches of curled textline detection use binarization as a pre-processing step, which can negatively affect the detection results under uneven shading. Furthermore, these approaches are sensitive to high degrees of curl and estimate x-line1 and baseline pairs using regression which may result in inaccurate estimation. We introduce a novel curled textline detection approach for grayscale document images. First, the textline structure is enhanced by using match filter bank smoothing and then central lines of textlines are detected using ridges. Then, x-line and baseline pairs are estimated by adapting active contours (snakes) over ridges. Unlike other approaches, our approach does not use binarization and applies directly on grayscale images. We achieved 91% of detection accuracy with good estimation of x-line and baseline pairs on the dataset of CBDAR 2007 document image dewarping contest.
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paper_title: Journals Subheadlines Text Extraction Using Wavelet Thresholding and New Projection Profile

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In this paper a new robust and efficient algorithm to automatic text extraction from colored book and journal cover sheets is proposed. First, we perform wavelet transform. Next for edge detecting from detail wavelet coefficient, we use dynamic threshold. By blurring approximate coefficients with alternative heuristic thresholding, achieve effective edge,. Afterward, with ROI technique get binary image. Finally text boxes would be extracted with new projection profile. Keywords— Text extraction; colored cover sheet; wavelet threshold, region of interest (ROI).
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paper_title: Identification of text on colored book and journal covers

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An approach to automatic text location and identification of colored book and journal covers is proposed. To reduce the amount of small variations in color, a clustering algorithm is applied in a preprocessing step. Two methods have been developed for extracting text hypotheses. One is based on a top-down analysis using successive splitting of image regions. The other is a bottom-up region growing algorithm. The results of both methods are combined to robustly distinguish between text and non-text elements. Text elements are binarized using automatically extracted information about text color. The binarized text regions can be used as input for a conventional OCR module. Results are shown for parts of book and journal covers of different complexity. The proposed method is not restricted to cover pages, but can be applied to the extraction of text from other types of color images as well.
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paper_title: Text extraction from gray scale historical document images using adaptive local connectivity map

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This paper presents an algorithm using adaptive local connectivity map for retrieving text lines from the complex handwritten documents such as handwritten historical manuscripts. The algorithm is designed for solving the particularly complex problems seen in handwritten documents. These problems include fluctuating text lines, touching or crossing text lines and low quality image that do not lend themselves easily to binarizations. The algorithm is based on connectivity features similar to local projection profiles, which can be directly extracted from gray scale images. The proposed technique is robust and has been tested on a set of complex historical handwritten documents such as Newton's and Galileo's manuscripts. A preliminary testing shows a successful location rate of above 95% for the test set.
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paper_title: Text Extraction from Document Images Using Edge Information

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Detection of text from documents in which text is embedded in complex colored document images is a very challenging problem. There are a lot of potential uses of text extraction in image searching, archiving documents etc. In this paper, we propose a simple edge based feature to perform this task. It aims at detecting textual regions from the document and separating it from the graphics portion. The algorithm is based on the sharp edges of the characters which are missing in images. We find these edges and use them to classify text from images. This edge information can also be used for other image interpretation tasks.
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paper_title: Text Extraction in Complex Color Document Images for Enhanced Readability

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Often we encounter documents with text printed on complex color background. Readability of textual contents in such documents is very poor due to complexity of the background and mix up of color(s) of foreground text with colors of background. Automatic segmentation of foreground text in such document images is very much essential for smooth reading of the document contents either by human or by machine. In this paper we propose a novel approach to extract the foreground text in color document images having complex background. The proposed approach is a hybrid approach which combines connected component and texture feature analysis of potential text regions. The proposed approach utilizes Canny edge detector to detect all possible text edge pixels. Connected component analysis is performed on these edge pixels to identify candidate text regions. Because of background complexity it is also possible that a non-text region may be identified as a text region. This problem is overcome by analyzing the texture features of potential text region corresponding to each connected component. An unsupervised local thresholding is devised to perform foreground segmentation in detected text regions. Finally the text regions which are noisy are identified and reprocessed to further enhance the quality of retrieved foreground. The proposed approach can handle document images with varying background of multiple colors and texture; and foreground text in any color, font, size and orientation. Experimental results show that the proposed algorithm detects on an average 97.12% of text regions in the source document. Readability of the extracted foreground text is illustrated through Optical character recognition (OCR) in case the text is in English. The proposed approach is compared with some existing methods of foreground separation in document images. Experimental results show that our approach performs better.
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paper_title: Text Regions Extracted from Scene Images by Ultimate Attribute Opening and Decision Tree Classification

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In this work we propose a method for localizing text regions within scene images consisting of two major stages. In the first stage, a set of potential text regions is extracted from the input image using residual operators (such as ultimate attribute opening and closing). In the second stage a set of features is obtained from each potential text region and this feature set will be later used as an input to a decision tree classifier in order to label these regions as text or non-text regions. Experiments performed using images from ICDAR public dataset show that this method is a good alternative for problems involving text location in scene images.
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paper_title: Enhanced Text Extraction from Arabic Degraded Document Images Using EM Algorithm

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This paper presents a new enhanced text extraction algorithm from degraded document images on the basis of the probabilistic models. The observed document image is considered as a mixture of Gaussian densities which represents the foreground and background document image components. The EM algorithm is introduced in order to estimate and improve the parameters of the mixtures of densities recursively. The initial parameters of the EM algorithm are estimated by the k-means clustering method. After the parameter estimation, the document image is partitioned into text and background classes by the means of ML approach. The performance of the proposed approach is evaluated on a variety of degraded documents comes from the collections of the National library of Tunisia.
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paper_title: Extracting captions from videos using temporal feature

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Captions in videos provide much useful semantic information for indexing and retrieving video contents. In this paper, we present an effective approach to extracting captions from videos. Its novelty comes from exploiting the temporal information in both localization and segmentation of captions. Since some simple features such as edges, corners and color are utilized, our approach is efficient. It involves four steps. First, we exploit the distribution of corners to spatially detect and locate the caption in a frame. Then the temporal localization for different captions in a video is performed by identifying the change of stroke directions. After that, we segment the caption pixels in a clip with a same caption based on the consistency and dominant distribution of caption color. Finally, the segmentation results are further refined. The experimental results on two representative movies have preliminarily verified the validity of our approach.
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paper_title: A Novel Model-based Segmentation Approach to Extract Caption Contents on Sports Videos

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The study proposes a novel scheme to extract and recognize the caption contents of various sports captions. A caption extraction process based on an iteratively temporal averaging technique is used to detect and locate a caption region in a series of video frames. Moreover, a caption-content extraction process based on caption identification and model-based segmentation processes is used to extract accurately the contents of various sports captions. Finally, some low-quality character images extracted from the caption contents are recognized using a commercial OCR. Experimental results show that the proposed model-based segmentation approach is very efficient to extract the contents of the various sports captions. Furthermore, the recognition performance from the application of the segmentation approach can be improved about 7.72% in test numeral set, compared to the projection-based segmentation method.
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paper_title: Caption text extraction for indexing purposes using a hierarchical region-based image model

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This paper presents a technique for detecting caption text for indexing purposes. This technique is to be included in a generic indexing system dealing with other semantic concepts. The various object detection algorithms are required to share a common image description which, in our case, is a hierarchical region-based image model. Caption text objects are detected combining texture and geometric features, which are estimated using wavelet analysis and taking advantage of the region-based image model, respectively. Analysis of the region hierarchy provides the final caption text objects.
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paper_title: Video caption detection and extraction using temporal information

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Video caption detection and extraction is an important step for information retrieval in video databases. In this paper, we extract text information in video by fully utilizing the temporal information contained in the video. First we create a binary abstract sequence from a video segment. By analyzing the statistical pixel changes in the sequence, we can effectively locate the (dis)appealing frames of captions. Finally we extract the captions to create a summary of the video segment.
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paper_title: A spatial-temporal approach for video caption detection and recognition

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We present a video caption detection and recognition system based on a fuzzy-clustering neural network (FCNN) classifier. Using a novel caption-transition detection scheme we locate both spatial and temporal positions of video captions with high precision and efficiency. Then employing several new character segmentation and binarization techniques, we improve the Chinese video-caption recognition accuracy from 13% to 86% on a set of news video captions. As the first attempt on Chinese video-caption recognition, our experiment results are very encouraging.
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paper_title: Robust Extraction of Text from Camera Images using Colour and Spatial Information Simultaneously

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The importance and use of text extraction from camera based coloured scene images is rapidly increasing with time. Text within a camera grabbed image can contain a huge amount of meta data about that scene. Such meta data can be useful for identification, indexing and retrieval purposes. While the segmentation and recognition of text from document images is quite successful, detection of coloured scene text is a new challenge for all camera based images. Common problems for text extraction from camera based images are the lack of prior knowledge of any kind of text features such as colour, font, size and orientation as well as the location of the probable text regions. In this paper, we document the development of a fully automatic and extremely robust text segmentation technique that can be used for any type of camera grabbed frame be it single image or video. A new algorithm is proposed which can overcome the current problems of text segmentation. The algorithm exploits text appearance in terms of colour and spatial distribution. When the new text extraction technique was tested on a variety of camera based images it was found to out perform existing techniques (or something similar). The proposed technique also overcomes any problems that can arise due to an unconstraint complex background. The novelty in the works arises from the fact that this is the first time that colour and spatial information are used simultaneously for the purpose of text extraction.
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paper_title: A new approach for video text detection

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Text detection is fundamental to video information retrieval and indexing. Existing methods cannot handle well those texts with different contrast or embedded in a complex background. To handle these difficulties, this paper proposes an efficient text detection approach, which is based on invariant features, such as edge strength, edge density, and horizontal distribution. First, it applies edge detection and uses a low threshold to filter out definitely non-text edges. Then, a local threshold is selected to both keep low-contrast text and simplify complex background of high-contrast text. Next, two text-area enhancement operators are proposed to highlight those areas with either high edge strength or high edge density. Finally, coarse-to-fine detection locates text regions efficiently. Experimental results show that this approach is robust for contrast, font-size, font-color, language, and background complexity.
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paper_title: Automatic caption localization in compressed video

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We present a method to automatically locate captions in MPEG video. Caption text regions are segmented from the background using their distinguishing texture characteristics. This method first locates candidate text regions directly in the DCT compressed domain, and then reconstructs the candidate regions for further refinement in the spatial domain. Therefore, only a small amount of decoding is required. The proposed algorithm achieves about 4.0% false reject rate and less than 5.7% false positive rate on a variety of MPEG compressed video containing more than 42,000 frames.
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paper_title: Text extraction and enhancement of binary images using cellular automata

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Text characters embedded in images represent a rich source of information for content-based indexing and retrieval applications. However, these text characters are difficult to be detected and recognized due to their various sizes, grayscale values, and complex backgrounds. Existing methods cannot handle well those texts with different contrast or embedded in a complex image background. In this paper, a set of sequential algorithms for text extraction and enhancement of image using cellular automata are proposed. The image enhancement includes gray level, contrast manipulation, edge detection, and filtering. First, it applies edge detection and uses a threshold to filter out for low-contrast text and simplify complex background of high-contrast text from binary image. The proposed algorithm is simple and easy to use and requires only a sample texture binary image as an input. It generates textures with perceived quality, better than those proposed by earlier published techniques. The performance of our method is demonstrated by presenting experimental results for a set of text based binary images. The quality of thresholding is assessed using the precision and recall analysis of the resultant text in the binary image.
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paper_title: Text extraction in complex color documents

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Text extraction in mixed-type documents is a pre-processing and necessary stage for many document applications. In mixed-type color documents, text, drawings and graphics appear with millions of different colors. In many cases, text regions are overlaid onto drawings or graphics. In this paper, a new method to automatically detect and extract text in mixed-type color documents is presented. The proposed method is based on a combination of an adaptive color reduction (ACR) technique and a page layout analysis (PLA) approach. The ACR technique is used to obtain the optimal number of colors and to convert the document into the principal of them. Then, using the principal colors, the document image is split into the separable color plains. Thus, binary images are obtained, each one corresponding to a principal color. The PLA technique is applied independently to each of the color plains and identifies the text regions. A merging procedure is applied in the final stage to merge the text regions derived from the color plains and to produce the final document. Several experimental and comparative results, exhibiting the performance of the proposed technique, are also presented.
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paper_title: Unsupervised categorization of heterogeneous text images based on fractals

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This paper deals about text extraction from heterogeneous documents for categorizing documents and indexing tasks. The purpose of this work is to find similar text regions basing on their fonts. First text regions are extracted, and then font matching is performed using fractal descriptors. Experiments are done for both maps and ancient documents.
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paper_title: Region-based caption text extraction

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This paper presents a method for caption text detection. The proposed method will be included in a generic indexing system dealing with other semantic concepts which are to be automatically detected as well. To have a coherent detection system, the various object detection algorithms use a common image description. In our framework, the image description is a hierarchical region-based image model. The proposed method takes advantage of texture and geometric features to detect the caption text. Texture features are estimated using wavelet analysis and mainly applied for Text candidate spotting. In turn, Text characteristics verification is basically carry out relying on geometric features, which are estimated exploiting the region-based image model. Analysis of the region hierarchy provides the final caption text objects. The final step of Consistency analysis for output is performed by a binarization algorithm that robustly estimates the thresholds on the caption text area of support.
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paper_title: Multiscale Edge-Based Text Extraction from Complex Images

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Text that appears in images contains important and useful information. Detection and extraction of text in images have been used in many applications. In this paper, we propose a multiscale edge-based text extraction algorithm, which can automatically detect and extract text in complex images. The proposed method is a general-purpose text detection and extraction algorithm, which can deal not only with printed document images but also with scene text. It is robust with respect to the font size, style, color, orientation, and alignment of text and can be used in a large variety of application fields, such as mobile robot navigation, vehicle license detection and recognition, object identification, document retrieving, page segmentation, etc.
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paper_title: Text Extraction and Document Image Segmentation Using Matched Wavelets and MRF Model

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In this paper, we have proposed a novel scheme for the extraction of textual areas of an image using globally matched wavelet filters. A clustering-based technique has been devised for estim ating globally matched wavelet filters using a collection of groundtruth images. We have extended our text extraction scheme for the segmentation of document images into text, background, and picture components (which include graphics and continuous tone images). Multiple, two-class Fisher classifiers have been used for this purpose. We also exploit contextual information by using a Markov random field formulation-based pixel labeling scheme for refinement of the segmentation results. Experimental results have established effectiveness of our approach.
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paper_title: Text Region Segmentation From Heterogeneous Images

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††† Summary Text in images contains useful information which can be used to fully understand images .This paper proposes an unified method to segment a text region from images such as Scene text images , Caption text & Document images using Contourlet transform . Contourlets not only possess the main features of wavelets (namely, multiscale and time-frequency localization), but also offer a high degree of directionality and anisotropy. It decomposes the image into set of directional sub bands with texture details capture in different orientations at various scales. As the contourlet transform is not shift-invariant , non subsampled contourlet transform NSCT is used here to extract text regions from heterogeneous images. Proposed system is tested on various kind of images & shown promising result .
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<format>
Title: A SURVEY ON VARIOUS APPROACHES OF TEXT EXTRACTION IN IMAGES

Section 1: INTRODUCTION
Description 1: Describe the context, motivation, and challenges of text extraction from images. Include examples and potential applications.

Section 2: REVIEW
Description 2: Provide an overview of the different approaches to text extraction from images. Classify based on image types and summarize key methods.

Section 3: Document Text Images
Description 3: Discuss approaches specifically used for text extraction from document text images. Review algorithms and techniques with examples.

Section 4: Scene Text Images
Description 4: Detail methodologies for extracting text from natural scene images. Include descriptions of key techniques and their specific challenges.

Section 5: Caption Text Images
Description 5: Outline methods for extracting caption text from videos and images. Summarize algorithms and their applications in various contexts.

Section 6: Heterogeneous Text Images
Description 6: Explain approaches used for text extraction from heterogeneous document images. Discuss methods that can handle diverse image types and text orientations.

Section 7: False Negatives
Description 7: Define False Negatives in the context of text extraction and discuss their implications on the performance of text extraction techniques.

Section 8: Precision rate
Description 8: Define Precision rate and explain its importance in evaluating text extraction algorithms.

Section 9: Recall rate
Description 9: Define Recall rate and discuss its role in the assessment of text extraction performance.

Section 10: F-score
Description 10: Define F-score and explain how it combines precision and recall to provide a comprehensive performance metric.

Section 11: CONCLUSION & FUTUREWORK
Description 11: Summarize the findings and insights from the survey. Discuss the limitations of current approaches and suggest directions for future research.
</format>
