A Review on Optical Character Recognition System

Transcription

1 A Review on Optical Character Recognition System Alka Kumari Nirupama Tiwari Abstract Optical Character Recognition (OCR) is a process that converts textual images into editable text documents. Nowadays, these systems are widely used in dematerialization applications such as mail sorting, invoice management, etc. In this context, the objective of this thesis is to propose an OCR system which ensures a better compromise between the recognition rate and the processing speed which makes it possible to make documents dematerialization reliable and real-time. To ensure recognition, the text is first extracted from the background. Then it is segmented into disjoint characters which will be described later based on their structural characteristics. Finally, the characters are recognized following the mapping of their descriptors to those of a predefined database. Despite the efforts and intensive work done in the field of optical recognition of writing, no OCR system is considered 100% reliable. The objective of this paper is to present a theoretical review on optical character recognition. Keywords GIF, JPF, JPG, LBP, OCR, PDF, TIF. I. INTRODUCTION The dematerialization of documents is a key process and widely used in the corporate world. It is defined as a form of exchange or preservation of information without physical support such as a paper mail, a check, a paper document from a book, a newspaper, or a magazine, etc. [1]. Optical Character Recognition (OCR) technology is a fundamental pillar for the dematerialization of documents. Once the document is scanned by a scanner, the text is extracted from the background and then segmented into disjoint characters. Subsequently, these characters are described and finally recognized. In heterogeneous documents, characters can be included in text regions with a seamless background, or in graphics, photographs, figures, tables, and so on [2]. A unique approach to extract the text regardless of the area of the document, homogeneous or complex, has problems. Thus, each type of zone has different characteristics which are similar to very specific image processing algorithms. These algorithms themselves have difficulties to isolate the text properly because of its complexity or the poor quality of the document (noise, brightness variation) [3]. Once the text is extracted, it is segmented into disjoint characters. Then, the similarities of each letter to a set of letters in a database are calculated. Recognition is done by selecting the smallest of similarities. This similarity requires an effective description of the characters not influenced by possible deformations such as the variation of scales, fonts, etc. [4]. OCR is a technology that converts different types of documents such as scanned paper documents, PDF files or digital photos into editable and actionable formats. Methodologically, the OCR proposes different approaches according to the mode of writing: manuscript or printed [5]. Two distinct domains are considered, this is the static recognition, still called "off-line", which works on a digital ink snapshot (on an image) and on-line dynamic recognition where the symbols are recognized as they are written by hand. OCR technology has been applied in recent years across the spectrum of industries revolutionizing the document management process [6]. OCR systems have enabled digitized documents to become fully searchable documents with text content that is recognized by computers. However, after more than two decades of research on the digitization of documents, these systems may still leave some imperfections to achieve a reissue of the document that it may be due to the various problems including the quality of the document and printing, discrimination of form, type of acquisition, variations in size, number of writers, size of vocabulary, etc. In the rest of the article, we define in section 2, the architecture of an OCR system and we present the approaches developed for each module of the system. Section 3 describes the approach taken to character recognition. Finally we finish in section 4 with a conclusion and perspectives.

2 II. OCR SYSTEM The goal of an OCR system is to recognize the text and then convert it into an editable form. Optical Character Recognition involves translating text in the image into modifiable character codes such as ASCII. The OCR systems offered by the different researchers are composed of a set of modules. The architecture of the system varies from one system to another as needed. The following system can be a generalization of all the proposed systems. Acquisition Pre-processing Segmentation Feature Extraction Classification Decision Figure 1: The steps of character recognition The image has entered the system and has undergone a set of pre-processing. It is segmented first into lines and characters. Then, a body and diacritic extraction of a character is performed. This step will have as output the diacritic and the body as well as the position of the diacritic with respect to the body for the case of the character with diacritics and will return the image of entry in the case of a character without diacritics. The recognition step consists of recognizing the extracted elements. The postprocessing is composed of two stages: first a combination between the diacritic and the body recognized to form the final character based on the position of the diacritic and the verification step by the user [7]. A. Acquisition In the acquisition phase, we use an optical scanner to scan the pages of the document and put them in the form of an image in the format jpg, gif or tif (types supported by the system). B. Pre-processing In the pre-processing phase, we choose to apply four different processings on the image to improve its quality. These four processings are: Binarization: transforms the input image into a binary image. In this step we perform a thresholding by OTSU method [8], by the average of the histogram and by the value of 127 which represents the mid-value of the gray levels, then a binarization with respect to the resulting threshold. Management: This is a technique used to frame the character in an image to eliminate the empty part of the image. Thus, we have proposed an algorithm for the frame which consists in traversing the image and eliminating its empty part to return an image containing only the character [9]. Standardization: This technique aims to standardize the size of the image. The segmentation phase gives as output images of different size from where the interest of making a normalization allowing to have a fixed size for all the images. The size chosen for our system is pixels. On the other hand, we have developed a method that normalizes the character size from the framed image by adding white pixels to have images of the same size without affecting the shape of the character [10]. Correction of Inclination: It is a technique which aims to correct the inclination of writing. This problem usually occurs in the acquisition phase. C. Segmentation The segmentation phase, consists firstly of separating the lines and then extracting the characters. This framework uses a vertical histogram to segment the text image into lines, then a horizontal histogram to extract the words and characters. D. Feature Extraction The feature extraction phase is a very important step in the OCR system. In our system, we have chosen local characteristics of the numeric / statistical type. These characteristics are the moment of Hu [11], the transformation of walsh [12] and the histogram of LBP (Local Binary Patterns) [13]. E. Classification The approach corresponds to the extracted characteristics which are of the numerical / statistical type. Classification in an OCR system brings together two tasks: learning and recognition and decision. At this stage the characteristics of the previous step are used

3 to identify a text segment and assign it to a reference model. F. Post-Processing The first step in the post-processing phase is to form the character by combining the body and the diacritic and then compose the word. The second step in our system is manual. The user is responsible for confirming the result obtained. III. LITERATURE REVIEW Gauri Katiyar et al. presented a handwritten character recognition framework on the basis of assessment of neural network by conjoining various feature extraction methods (Mean and Gradient Operations, Box Approach and Diagonal Distance Approach). The CEDAR (Centre of Excellence for Document Analysis And Recognition) dataset was taken for proposed framework [14] Gaur et al. presented a three phase approach for recognition of characters. Pre-processing is performed in the initial phase which contains image binarization and characters separations. The second phase is feature extraction which is accomplished by k-means clustering and the feature vector is generated. Third and final phase is classification which is performed by support vector machine (SVM) [15]. These days character recognition has picked up a considerable measure of consideration in the arena of pattern recognition because of its usage in different aspects. Handwritten Character Recognition (HCR) and Optical Character Recognition (OCR) has particular space to relate. OCR framework is most reasonable for the uses like multi decision investigations, published mailing address determination and so on. While utilization of HCR is more extensive contrasted with OCR. HCR is helpful in a wide range of form processing frameworks and a great deal more. In future, character recognition framework may assist as a key component to make a paperless domain by digitizing and handling current paper forms. Sameeksha Barve gave a neural network based approach to deal with recognition of optical or visual characters. OCR (Optical Character Recognition) System or to enhance the accuracy of a current one. The utilization of artificial neural network rearranges advancement of an optical character recognition application, while accomplishing most elevated accuracy of recognition and great performance [16]. Sameeksha Barve exhibited an Optical character recognition framework in view of Artificial Neural Networks (ANNs). Training of neural network is accomplished by back propagation algorithm [17]. Handwritten character recognition is dependably an outskirts region of research in the field of pattern recognition and there is an expansive interest for Optical Character Recognition on hand written archives. Shabana et al. give an exhaustive survey of existing works in handwritten character recognition in light of soft computing strategy during the previous decade [18]. Patel et al. suggested a technique for the recognition of handwritten characters. The multiresolution approach is accomplished using DWT and Euclidean distance formula. This approach was found to be faster in the form of 26 patterns of characters. DWT features of handwritten characters are extracted with multiresolution approach, thereafter a mean vector is derived for every pattern class. Euclidean distance is measured between input vector and the mean vectors. The membership function for input vector is accomplished by the minimum distance. This technique gives great recognition precision of 90% [19]. R. K. Mandal et al. proposed another strategy to enhance the execution of the beforehand connected strategies. The input image matrix is compacted into a lower dimension matrix with a specific end goal to lessen non-significant features of the image matrix. The compressed matrix is segmented column-wise. Every segment of a specific image matrix is mapped to indistinguishable patterns for perceiving a specific character. The larger part of a known pattern chooses the presence of a specific character [20]. Pramod J Simha et al. exhibited the abilities of Artificial Neural Network usage in recognition of extended sets of optical language images. This paper portrays a propelled arrangement of classification utilizing probabilistic neural systems. Training of the classifier begins with the utilization of mutilation displayed characters from text styles. Factual measures are taken up against an arrangement of features figured from the twisted character [21]. Mitrakshi B. Patil et al. presented an offline recognition technique for characters using ANN systems. This approach is divided into two phases; the primary phase is the separation of characters into the line, word and characters while the secondary phase utilized the feed-forward neural network algorithm for recognition of chatacters [22]. Tirtharaj Dash et al. built up an Offline Hand Written English Character Recognition in light of Artificial Neural Network (ANN). The ANN executed in this work has single yield neuron which indicates whether the test character has a place with

4 a specific cluster or not. The execution is done totally in "C" language. Ten arrangements of English letter sets (little 26, capital-26) were utilized to prepare the ANN and 5 sets of English letter sets were utilized to test the system. The characters were gathered from several persons over a span of nearby 25 days. The approach was tested with the sets of having 5 small letters and 5 capital letters [23]. Amarjot Singh et al. depicted an overview of uses of OCR in various fields and further exhibits the experimentation for three essential applications, for example, Captcha, Institutional Repository and Optical Music Character Recognition. This paper makes utilization of an improved image segmentation approach in light of histogram equalization utilizing genetic algorithms for optical character recognition [24]. J. Pradeep et al. depicted an offline handwritten alphabetical character recognition framework utilizing multilayer feed forward neural network system. A strategy, called, diagonal based feature extraction is presented for separating the features of the manually written letter sets. Fifty information sets, each containing 26 letters in order composed by different individuals, are utilized for preparing the neural system and 570 diverse handwritten in order characters are utilized for testing [25]. IV. CONCLUSION This paper described OCR system and its modules including the steps of acquisition, pre-processing, segmentation, feature extraction, classification and post-processing as well as the approaches developed for each module. This procedure is done generally in the post-offices to automatically read the addresses and names and on wrappers and by the banks to peruse acknowledge the amount and number of checks. Likewise, different organizations and persons can utilize this technique to rapidly make an interpretation of paper reports to computer written documents. 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