Text Extraction matlab code

ABSTRACT

Text provides important information about images or video sequences in a documented image, but it always remains difficult to modify the static documented image. To carry out modification in any of the text matter the text must be segmented out from the documented image, which can be used for further analysis. Taking consideration to video image sequence the isolation of text data from the isolated frame becomes more difficult due to its variable nature.

Various methods were proposed for the isolation of text data from the documented image. Among which Wavelet transforms have been widely used as effective tool in text segmentation. Document images usually contain three types of texture information. various wavelet transformation have been propsed for the decomposition of these images into their fundamentals feature. Onto these wavelet families, it is one of the difficult task in selecting a proper wavelet transformation with proper scale level for text isolation.

This project work implements an efficient text isolation algorithm for the extraction of text data from the documented video clips. the implemented system carries out an performance analysis on various wavelet transforms for the proper selection of wavelet transform with multi level decomposition. Of the selected wavelet transform the obtained wavelet coefficients are applied with morphological operators for text isolation and evaluates the contribution of decomposition levels and wavelet functions to the segmentation result in documented vedio image. The proposed task implements neural network for the recognision of text characters from the isolated text image for making it editable. The proposed work is implemented using Matlab tool.

INTRODUCTION

1.1 OVERVIEW

Documents have been the traditional medium for printed documents. However, with the advancement of digital technology, it is seen that paper documents were gradually augmented by electronic documents. Paper documents consist of printed information on paper media. Electronic documents use predefined digital formats, where information regarding both textual and graphical document elements, have been recorded along with layout and stylistic data. Both paper and electronic documents confer to their own advantages and disadvantages to the user. For example, information on paper is easy to access but tedious under modification and difficult under storage of huge information. While electronic documents are best under storage of huge data base but very difficult for modifications.

In order to gain the benefits of both media, the user needs to be able to port information freely between the two formats. Due to this need, the development of computer systems capable of accomplishing this interconversion is needed. Therefore, Automatic Document Conversion has become increasingly important in many areas of academicia, business and industry. Automatic Document Conversion, occurs in two directions: Document Formatting and Document Image Analysis. The first automatically converts Electronic documents to paper documents, and the second, converts paper documents to their electronic counterparts.

Document Image Analysis is concerned with the problem of transferring the document images into electronic format. This would involve the automatic interpretation of text images in a printed document, such as books, reference papers, newspapers etc. Document Image Analysis can be defined as the process that performs the overall interpretation of document images. It is a key area of research for various applications in machine vision and media processing, including page readers, content-based document retrieval, digital libraries etc.

There is a considerable amount of text occurring in video that is a useful source of information, which can be used to improve the indexing of video. The presence of text in a scene, to some extent, naturally describes its content. If this text information can be harnessed, it can be used along with the temporal segmentation methods to provide a much truer form of content-based access to the video data.

Figure 1.1 Example of a documented video image clip

Text detection and recognition in videos can help a lot in video content analysis and understanding, since text can provide concise and direct description of the stories presented in the videos. In digital news videos, the superimposed captions usually present the involved person’s name and the summary of the news event. Hence, the recognized text can become a part of index in a video retrieval system.

1.2 STATEMENT OF PROBLEM

Text in images and video sequences provide highly condensed information about the contents of the images or video sequences and can be used for video browsing in a large video database. Text superimposed on the video frames provides supplemental but important information for video indexing and retrieval. Although text provides important information about images or video sequences, it is not a easy problem to detect and segment them. The main difficulties lie in the low resolution of the text, and the complexity of the background. Video frames have very low resolution and suffer from blurring effects due to lossy compression. Additionally the background of a video frame is more complex with many objects having text like features. One more problem lies with the handling of large amount of text data in video clip images.

1.3 OBJECTIVE OF THE STUDY

The main objective of this project is to develop an efficient text extraction system for the localization of text data from the video image sequence. The project also aims in recognizing the extracted text data and make it editable for further modifications. The project implemented, performs transformation analysis on existing wavelet transforms for the suitability of wavelet transformation for text isolation having multiple features. The project realizes morphological operation on the wavelet coefficients and presents an efficient approach, to the recognition of text characters from the isolated documented video image making it editable for further modifications.

1.4 REVIEW OF LITERATURE

Many efforts have been made for text extraction and recognition in video image sequence. Chung-Wei Liang and Po-Yueh Chen [1] in their paper DWT Based Text Localization presents an efficient and simple method to extract text regions from static images or video sequences. They implemented Haar Discrete Wavelet Transform (DWT) with morphological operator to detect edges of candidate text regions for isolation of text data from the documented video image.

A Video Text Detection And Recognition System presented by Jie Xi 1, Xian-Sheng Hua , Xiang-Rong Chen , Liu Wenyin , Hong-Jiang Zhang [2] proposed a new system for text information extraction from news videos. They developed a method for text detection and text tracking to locate text areas in the key-frames. Xian-Sheng Hua, Pei Yin , Hong-Jiang Zhang in their paper Efficient video text recognition using Multiple frame integration [3] presented efficient scheme to deal with multiple frames that contain the same text to get clear word from isolated frames.

C´eline Thillou and Bernard Gosselin proposed a thresholding method for degraded documents acquired from a low-resolution camera [4]. They use the technique based on wavelet denoising and global thresholding for nonuniform illumination. In their paper Segmentation-based binarization for color-degraded images [5] they described the stroke analysis and character segmentation for text segmentation. They proposed the binarization method to improve character segmentation and recognition.

S. Antani and D. Crandall in their paper Robust Extraction of Text in Video [7] describes an update to the prototype system for detection, localization and extraction of text from documented video images. Rainer Lienhart and Frank Stuber presented an algorithm for automatic character segmentation for motion pictures in their paper ‘Automatic text recognition in digital videos’ [9], which extract automatically and reliably the text in pre-title sequences, credit titles, and closing sequences with title and credits. The algorithm uses a typical characteristic of text in videos in order to enhance segmentation and recognition.

Jovanka Malobabiæ, Noel O'Connor, Noel Murphy, Sean Marlow in there paper Automatic Detection and Extraction of Artificial Text in Video, [12] proposed an algorithm for detection and localization of artificial text in video image using a horizontal difference magnitude measure and morphological processing.

1.5 SCOPE OF STUDY

This project implements an efficient system for the extraction of text from a given away documented video clips and recognizes the extracted text data for further applications. The implemented project work finds efficient usage under video image processing for enhancement and maintenance. The work can be efficiently used in the area of video image enhancement such as cinematography and video presentation etc. The proposed work will be very useful under digital library maintance of video database.

Following are the areas of application of text isolation and recognition in video images;

1. Digital library: For maintenance of documented video images in large database.
2. Data modification: Useful under modification of informations in video images.
3. Cinematographic applications: For enhancing the document information in movie video clips.
4. Instant documentation of news and reports: For documentization of instant reports and news matters in paper.

1.6 METHODOLGY

Many efforts have been made earlier to address the problems of text area detection, text segmentation and text recognition. Current text detection approaches can be classified into three categories:

The first category is connected component-based method, which can locate text quickly but have difficulties when text is embedded in complex background or touches other graphical objects.

The second category is texture-based, which is hard to find accurate boundaries of text areas and usually yields many false alarms in “text-like” background texture areas.

The third category is edge-based method. Generally, analyzing the projection profiles of edge intensity maps can decompose text regions and can efficiently predict the text data from a given video image clip.

Text region usually have a special texture because they consist of identical character components. These components contrast the background and have a periodic horizontal intensity variation due to the horizontal alignment of many characters. As a result, text regions can be segmented using texture feature.

1.6.1 DOCUMENT IMAGE SEGMENTATION

Document Image Segmentation is the act of partitioning a document image into separated regions. These regions should ideally correspond to the image entities such as text blocks and graphical images, which are present in the document image. These entities can then be identified and processed as required by the subsequent steps of Automated Document Conversion.

Various methods are described for processing Document Image Segmentation. They include: Layout Analysis, Geometric Structure Detection/Analysis, Document Analysis, Document Page Decomposition, Layout Segmentation, etc. Texts in images and video sequences provide highly condensed information about the contents of the images or video sequences and can be used for video browsing/retrieval in a large image database. Although texts provide important information about images or video sequences, it is not easy to detect and segment out the text data from the documented image.

The difficulty in text extraction is due to the following reasons;

1. The text properties vary randomly with non-uniform distribution.

2. Texts present in an image or a video sequence may have different cluttered background.

Methods for text extraction can be done using component-based or texture-based. Using component-based texts extraction methods, text regions are detected by analyzing the edge component of the candidate regions or homogenous color/grayscale components that contain the characters. Whereas texture based method uses the texture property such as curviness of the character and image for text isolation. In texture based document image analysis an M-band wavelet transformation is used which decomposes the image into M×M band pass sub channels so as to detect the text regions easily from the documented image. The intensity of the candidate text edges are used to recognize the real text regions in an M-sub band image.

1.6.2 WAVELET TRANSFORMATION

Digital image is represented as a two-dimensional array of coefficients, each coefficient representing the intensity level at that coordinate. Most natural images have smooth color variations, with the fine details being represented as sharp edges in between the smooth variations. Technically, the smooth variations in color can be termed as low frequency variations, and the sharp variations as high frequency variations.

The low frequency components (smooth variations) constitute the base of an image, and the high frequency components (the edges which give the details) add upon them to refine the image, thereby giving a detailed image. Hence, the smooth variations are more important than the details.

Separating the smooth variations and details of the image can be performed in many ways. One way is the decomposition of the image using the discrete wavelet transform. Digital image compression is based on the ideas of sub-band decomposition or discrete wavelet transforms. Wavelets, which refer to a set of basis functions, are defined recursively from a set of scaling coefficients and scaling functions. The DWT is defined using these scaling functions and can be used to analyze digital images with superior performance than classical short-time Fourier-based techniques, such as the DCT.

1.6.3 MORPHOLOGICAL OPERTION

Mathematical morphology as a tool for extracting image components that are useful in the representation and descriptive of region shape, such as boundaries, skeletons and the convex hull. It is defined two fundamental morphological operations, dilation and erosion, in terms of the union or intersection of an image with a translated shape called as structuring element.

1.6.4 CHARACTER RECOGNITION

The essential problem of character recognition is to identify an object as belonging to a particular group. Assuming that the objects associated with a particular group share common attributes more than with objects in other groups, the problem of assigning an unlabeled object to a group can be accomplished by determining the attributes of the object called as features. If information about all possible objects and the groups to which they are assigned is known, then the identification problem is straightforward, i.e., the attributes that is best discriminate among groups and the mapping from attributes to groups can be determined with certainty.

Given the goal of classifying objects based on their attributes, the functionality of an automated character recognition system can be divided into two basic tasks:

a) The description task generates attributes of an object using feature extraction techniques.

b) The classification task assigns a group label to the object based on those attributes with a classifier.

The description and classification tasks work together to determine the most accurate label for each unlabeled object analyzed by the character recognition system. This is accomplished with a training phase that configures the algorithms used in both the description and classification tasks based on a collection of objects whose labels are known--i.e., the training set. During the training phase, a training set is analyzed to determine the attributes and mapping which assigns labels to the objects in the training set with the fewest errors. Once trained, a character recognition system assigns a classification to an unlabeled object by applying the mapping to the attributes of that object. A measure of the efficacy of a trained character recognition system can be computed by comparing the known labels with the labels assigned by the classification task to the training set: as the agreement between known and assigned labels increases, the accuracy of the character recognition system increases. Such a methodology for configuring and evaluating the description and classification tasks of a character recognition system is called supervised learning.

1.7 LIMITATION OF STUDY

This project work implements a text isolation and recognition system for the isolation of text character from a given video sequence. The project work implemented has certain limitation on the implementation. The implemented system gives less accuracy under high intensity background of video image. The implementation also shows less accuracy to the extraction of text and recognition under occultation. Under high variable components in video sequence the system results in text isolation with noise.

IMPLEMENTATION

5.1 FUNCTION DESCRIPTION

For the implementation of the proposed design following functions are realized:

ANML : This function analyzes the multilevel decomposition of a document image in multiple levels. This function reads the processing, time, percentage error and plot the comparid in plot .

ANMW : This function analyzes the performance of multiple wavelets for documented image by plotting the comparid on plot for computation time and % of error.

CLOSE1 : This function responds to the closed button of user interface and closes all the active figure windows.

CR : This function used to recognize the character from the isolated text.

DB123 : This function realizes the decomposition of a documenter image using Debuchie wavelet transform.

DBRES : This function is implemented for displaying the obtained result of Debuchie wavelet decomposition.

FINAL : This function gives the top level user interface to the implemented modules.

FOUR1 : This function determines the co-occurrence vector for this image passed using 45 degrees scanning.

GUI : The second level graphical user interface created for user interaction.

HR : This function realizes the decomposition of a documenter image using haar wavelet transform.

HRRES : This function is implemented for displaying the obtained result of haar wavelet decomposition.

ML : This function performs multilevel decomposition of the documented image. This function decomposes the image at four different scale levels.

MW : This function is implemented to perform wavelet decomposition of documented image using 3 different wavelet transforms.

NINE1 : This function determines the co-occurrence vector for this image passed using 90 degrees scanning.

OTHRE : This function determines the co-occurrence vector for this image passed using 135 degrees scanning.

PROCESS : The graphical user interfaces which call back processing of wavelets and multilevel decomposition.

READ1 : This function is implemented for reading the documented image stored in the work space.

SC1 : This function scale down the image in one level returning 3 detail and one appropriate coefficient.

SC2 : This function scale down the image in two level returning 3 detail and one appropriate coefficient.

SC3 : This function scale down the image in three level returning 3 detail and one appropriate coefficient.

SC4 : This function scale down the image in two level returning 3 detail and one appropriate coefficient.

SCR1 : This function displays the multi scaled image in hierarchal order of level one.

SCR2 : This function displays the multi scaled image in hierarchal order of level two.

SCR3 : This function displays the multi scaled image in hierarchal order of level three.

SCR4 : This function displays the multi scaled image in hierarchal order of level four.

SCRR1 : This function displays the isolated text result at level-1.

SCRR2 : This function displays the isolated text result at level-2.

SCRR3 : This function displays the isolated text result at level-3.

SCRR4 : This function displays the isolated text result at level-4.

SPL1 : This function realizes the decomposition of a documenter image using spline wavelet transform.

SPLINERES : This function is implemented for displaying the obtained result of spline wavelet decomposition.

TEST : This function is implemented for optimal segmentation of documented image for selected wavelet and scale level.

Tr : This function compares the two trained data one of the test sample and the other pre-defined data and returns the status of reorganization.

TRAINF00 : This function reads the co-occurrence value and calculates the 7 features namely contrast, energy, entropy, local homogeneity, cluster shade and minimum possibility cluster prominent and train for zero order scanning.

TRAINF45 : This function reads the co-occurrence value and calculates the 7 features namely contrast, energy, entropy, local homogeneity, cluster shade and minimum possibility cluster prominent and train for zero order scanning.

TRAINF90 : This function reads the co-occurrence value and calculates the 7 features namely contrast, energy, entropy, local homogeneity, cluster shade and minimum possibility cluster prominent and train for zero order scanning.

TRAINF135 : This function reads the co-occurrence value and calculates the 7 features namely contrast, energy, entropy, local homogeneity, cluster shade and minimum possibility cluster prominent and train for zero order scanning.

TRAIN SAMPLE : This function trains the input sample for optimize trained database. These values used as knowledge for isolating the characters.

TRAIN TEST : This function is used for integrating the training in all four quadrants for the test sample as well as the optimized sample.

VR : This function is used to view the obtained results return due to processing using optimized wavelet and optimized level.

WAVE2GRAY : This function is implemented for aligning the scaled image in wavelet hierarchy.

WAVE COPY : This function aligns the isolated co-efficient after wavelet decomposition.

WAVE CUT : This function is used to isolate the decomposed co-efficient and appropriate co-efficient.

WAVE FAST : This function is realized for wavelet decomposition of the image sample.

WAVE FILTER : The wavelet filters for high pass and low pass filter are defined in this function.

WAVE WORK : Call wave cut and wave copy for aligning the decomposed co-efficient in wavelet hierarchy.

ZERO1 : This function determines the co-occurrence vector for this image passed using 10 degrees scanning.

CONCLUSION

The project work realizes an efficent text segmentation algorithm and character recognition for the isolated text data in a documented video image sequence. The text isolation system implements three wavelet transformations namely Harr, Debuchie and spline wavelet and analyzes the effect of these wavelet transformation on text isolation process for a given video sequence. The system also analyze the effect of level decomposition on a documented video image sequence. The obtained results shows good isolation of text data form the image sequance for biorthogonal spline wavelet and shows better result at fourth level of decomposition. From the multi level and mult wavlet analysis the best suited wavelet transform and the best level of decomposition is obtained which is used under text isolation and its recogintion. The spline wavelet transformation gives mopre accuracy to isolation of text data compared to haar and debuchie wavlet transform at higher level of decomposition. The implemented design also realizes the character recognition unit using supervised learning prcoess. It is observed that the system recognizes theisolated text data form the video sequence to high accuracy.