DOCSLIB.ORG

Steps Involved in Text Recognition and Recent Research in OCR; a Study

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Steps Involved in Text Recognition and Recent Research in OCR; a Study International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8, Issue-1, May 2019 Steps Involved in Text Recognition and Recent Research in OCR; A Study K.Karthick, K.B.Ravindrakumar, R.Francis, S.Ilankannan Abstract: The Optical Character Recognition (OCR) is one of Preprocessing the automatic identification techniques that fulfill the automation Feature Extraction needs in various applications. A machine can read the Recognition information present in natural scenes or other materials in any Post processing form with OCR. The typed and printed character recognition is uncomplicated due to its well-defined size and shape. The In addition to the above steps segmentation and handwriting of individuals differs in the above aspects. So, the morphological processing also involved in the recognition handwritten OCR system faces complexity to learn this difference process. These steps may be added before the feature to recognize a character. In this paper, we discussed the various extraction process. stages in text recognition, handwritten OCR systems classification according to the text type, study on Chinese and II. PREPROCESSING Arabic text recognition as well as application oriented recent research in OCR. The preprocessing is a fundamental stage that is proceeding Index Terms: Edge detection, Optical Character Recognition, OCR, Preprocessing stages, Text Recognition to the stage of feature extraction; it regulates the appropriateness of the outcomes for the consecutive stages. I. INTRODUCTION The OCR success rate is contingent on the success percentage of each stage. The technologyadventfoundan amazing and noble development curve for the last two centuries. For the last A. Factors Affecting the Text Recognition Quality few decades, it is easy in using mouse and keyboard to assist as interfacing device between us and computer. However, Many factors influence the precision of character recognized the human-based communications ability to interact with a using OCR. The factors are scan resolution, scanned image computer would make things easier to handler, but would be quality, printed documents category either photocopied or difficult to succeed for the investigators / researchers. laser printer, quality of the paper, and linguistic Thepioneering achievements due to continuous research in complexities. The uneven illumination and watermarks are man-machine communication may bring the scenario like few factors faced in OCR system that influence the accuracy interactions between human. Various techniques using of OCR. magnetic stripe, speech recognition, identification using radio frequency, bar code, and Optical Mark Recognition B. Significance of Preprocessing in Text Recognition (OMR), and OCR fulfill the automation needs in various applications. In this paper, we discussed about the The preprocessing step is necessary to obtain better text handwritten OCR systems classification and the steps recognition rate, using efficient algorithms of preprocessing involved in OCR that is one of the automatic identification creates the text recognition method robust using noise technique. removal, image enhancing process, image threshold process, skewing correction, page and text segmentation, text normalization and morphological operations. C. Preprocessing methods The majority of OCR application uses binary / gray images. The images mayhave watermarks and/or non-uniform background that make recognition process difficult without performing the preprocessing stage. There are several steps Fig.1 Steps Involved in Text Recognition needed to achieve this. The initial step is to adjust the The low-level image processing involves primitive contrast or to eliminate the noise from the image called as operation such as to reduce noise, contrast, enhancement the image enhancement technique. The next step is to do and image sharpening. The major steps involved in text thresholding for removing the watermarks and/or noise recognition system are shown in Figure 1.They are, followed by the page segmentation for isolating the graphics Revised Manuscript Received on May 22, 2019 from the text. The next step is text segmentation to K.Karthick, Associate Professor, Department of EEE, GMR Institute individual character separation followed by morphological of Technology, Rajam. K.B.Ravindrakumar ,Professor, Department of EEE, Vel Tech Multi processing. The morphological processing is required to add Tech Dr.RangarajanDr.Sakunthala Engineering College, Avadi, pixels if the preprocessed image has eroded parts in the R.Francis Assistant Professor, Department of Electrical Engineering, characters. Annamalai University, Chidambaram, S.Ilankannan,Research Scholar, Department of Electrical Engineering,Annamalai University. Published By: Retrieval Number A2670058119/19©BEIESP Blue Eyes Intelligence Engineering & 3095 Sciences Publication Steps Involved in Text Recognition and Recent Research in OCR; A Study D. Techniques involved in Image Enhancement Image enhancement increasesimage quality for perception of humans by increasing contrast, minimizing blurring and removing noise (Nithyananda et al. 2016). E. Spatial Image Filtering The filters are applied to defeat the high or low frequencypresent in the image. Eliminating the high frequencies in the image is smoothing, and the low frequencyelimination is enhancing or edge detection in the image. The following figure 2(a) shows the original image and 2 (b) & (c) shows the images applied withPrewitt and Canny edge detection methods. These filtering techniques may give effective text detection from images available in natural scene. (b) (a) (c) Fig.2 Edge Detection (a) Original Image(b) Prewittmethod (c) Cannymethod Spatial Image Filtering Process Point Mask processing processing techniques techniques Log Power law Global Contrast Histogram Averaging Sharpening Local transformati transformati thresholding stretching equalization filters filters thresholding -ons -ons Fig. 3 Spatial filtering category Spatial image filtering processing is categorized into mask Global thresholding: Image thresholding is the method of and point processing. Figure 3 shows the spatial filtering isolating the information from its background. Hence, this categories. Point processing (Yi & Li 2013) adjusts the pixel method is usednormallyto grey-level, or scanned color value present in the image to generate the corresponding images and itis categorized as global and localthresholding. pixel value in the enhanced output image is expressed as, Globalmethod of thresholding chooses avalue of threshold for the completeimage from the intensity histogram (Mori O (a, b) = T [I (a, b)] (1) 2010). Global thresholdingautomatically reduces a grey- level image to a binary image. The local adaptive Where, I (a, b) is the input image, O (a, b) is the enhanced thresholding method for each pixel it uses different values output image and T denotes the transformation among them. based on the information of local area. The figure 4 shows F. Techniques of Point processing the global threshold applied using Otsu’s method. Published By: Retrieval Number A2670058119/19©BEIESP 3096 Blue Eyes Intelligence Engineering & Sciences Publication International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8, Issue-1, May 2019 Contrast stretching method: The image contrast level may change because of incorrect setting or the poor illumination in the acquisition process.The equation of linear mapping expressed as, - - (2) - Where A1tends to 0 and A2denotes to desired level. I1 and I2are the min and max values of the input gray range (Mori 2010). Fig. 4 Global thresholding using Otsu's method Log transformations: The common form of this method is expressed as, S = K log (1+r) (3) Here 'K' is a constant and the assumption is r ≥ 0. (Gonzalez et. al. 2009). (a) (b) (c) (d) Fig. 5 (a) Original Image (b) Contrast Enhancement using understanding problems to normalize variations in Histogram Equalization (c) Original imageHistogram(d) illumination.For each level of brightness 'j' in the actual Histogram of the processed image image, the level value (k) for the new pixel can be calculated as, Histogram equalization: Histogram equalization is a global i 0 i type method thatgives the histogram of the whole pixels (0- (4) 255) range and this technique can be applied in image Published By: Retrieval Number A2670058119/19©BEIESP 3097 Blue Eyes Intelligence Engineering & Sciences Publication Steps Involved in Text Recognition and Recent Research in OCR; A Study WhereT is thetotal pixels in an image (Russ 2007). The selecting or searching most relevant features (Mohamad et contrast enhancement of an image using histogram al. 2015). equalization and its histogram has been shown in figure 5. Feature set should have a sufficient discriminating power to G. Mask Processing Techniques enable the accurate classification even among very similar symbols. Thus, features which are beneficial for Averaging filters: Average filter also known as mean filter classification ensure that objects from different classes have is auncomplicated method for smoothing images.It different values; objects from the identical class have similar minimizes the intensity variation amount between adjacent feature values. A good feature set should be efficient about pixels also used to decrease or remove the noise. The computation time. averaging filter will be acting as a low-pass frequency filterthatreduces the intensity of spatial derivatives in the K.
Load more

Recommended publications
  • Sentence Boundary Detection for Handwritten Text Recognition Matthias Zimmermann
    Sentence Boundary Detection for Handwritten Text Recognition Matthias Zimmermann To cite this version: Matthias Zimmermann. Sentence Boundary Detection for Handwritten Text Recognition. Tenth International Workshop on Frontiers in Handwriting Recognition, Université de Rennes 1, Oct 2006, La Baule (France). inria-00103835 HAL Id: inria-00103835 https://hal.inria.fr/inria-00103835 Submitted on 5 Oct 2006 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Sentence Boundary Detection for Handwritten Text Recognition Matthias Zimmermann International Computer Science Institute Berkeley, CA 94704, USA [email protected] Abstract 1) The summonses say they are ” likely to persevere in such In the larger context of handwritten text recognition sys- unlawful conduct . ” <s> They ... tems many natural language processing techniques can 2) ” It comes at a bad time , ” said Ormston . <s> ”A singularly bad time ... potentially be applied to the output of such systems. How- ever, these techniques often assume that the input is seg- mented into meaningful units, such as sentences. This pa- Figure 1. Typical ambiguity for the position of a sen- per investigates the use of hidden-event language mod- tence boundary token <s> in the context of a period els and a maximum entropy based method for sentence followed by quotes.
    [Show full text]
  • Multiple Segmentations of Thai Sentences for Neural Machine Translation
    Proceedings of the 1st Joint SLTU and CCURL Workshop (SLTU-CCURL 2020), pages 240–244 Language Resources and Evaluation Conference (LREC 2020), Marseille, 11–16 May 2020 c European Language Resources Association (ELRA), licensed under CC-BY-NC Multiple Segmentations of Thai Sentences for Neural Machine Translation Alberto Poncelas1, Wichaya Pidchamook2, Chao-Hong Liu3, James Hadley4, Andy Way1 1ADAPT Centre, School of Computing, Dublin City University, Ireland 2SALIS, Dublin City University, Ireland 3Iconic Translation Machines 4Trinity Centre for Literary and Cultural Translation, Trinity College Dublin, Ireland {alberto.poncelas, andy.way}@adaptcentre.ie [email protected], [email protected], [email protected] Abstract Thai is a low-resource language, so it is often the case that data is not available in sufficient quantities to train an Neural Machine Translation (NMT) model which perform to a high level of quality. In addition, the Thai script does not use white spaces to delimit the boundaries between words, which adds more complexity when building sequence to sequence models. In this work, we explore how to augment a set of English–Thai parallel data by replicating sentence-pairs with different word segmentation methods on Thai, as training data for NMT model training. Using different merge operations of Byte Pair Encoding, different segmentations of Thai sentences can be obtained. The experiments show that combining these datasets, performance is improved for NMT models trained with a dataset that has been split using a supervised splitting tool. Keywords: Machine Translation, Word Segmentation, Thai Language In Machine Translation (MT), low-resource languages are 1. Combination of Segmented Texts especially challenging as the amount of parallel data avail- As the encoder-decoder framework deals with a sequence able to train models may not be enough to achieve high of tokens, a way to address the Thai language is to split the translation quality.
    [Show full text]
  • A Clustering-Based Algorithm for Automatic Document Separation
    A Clustering-Based Algorithm for Automatic Document Separation Kevyn Collins-Thompson Radoslav Nickolov School of Computer Science Microsoft Corporation Carnegie Mellon University 1 Microsoft Way 5000 Forbes Avenue Redmond, WA USA Pittsburgh, PA USA [email protected] [email protected] ABSTRACT For text, audio, video, and still images, a number of projects have addressed the problem of estimating inter-object similarity and the related problem of finding transition, or ‘segmentation’ points in a stream of objects of the same media type. There has been relatively little work in this area for document images, which are typically text-intensive and contain a mixture of layout, text-based, and image features. Beyond simple partitioning, the problem of clustering related page images is also important, especially for information retrieval problems such as document image searching and browsing. Motivated by this, we describe a model for estimating inter-page similarity in ordered collections of document images, based on a combination of text and layout features. The features are used as input to a discriminative classifier, whose output is used in a constrained clustering criterion. We do a task-based evaluation of our method by applying it the problem of automatic document separation during batch scanning. Using layout and page numbering features, our algorithm achieved a separation accuracy of 95.6% on the test collection. Keywords Document Separation, Image Similarity, Image Classification, Optical Character Recognition contiguous set of pages, but be scattered into several 1. INTRODUCTION disconnected, ordered subsets that we would like to recombine. Such scenarios are not uncommon when The problem of accurately determining similarity between scanning large volumes of paper: for example, one pages or documents arises in a number of settings when document may be accidentally inserted in the middle of building systems for managing document image another in the queue.
    [Show full text]
  • An Incremental Text Segmentation by Clustering Cohesion
    An Incremental Text Segmentation by Clustering Cohesion Raúl Abella Pérez and José Eladio Medina Pagola Advanced Technologies Application Centre (CENATAV), 7a #21812 e/ 218 y 222, Rpto. Siboney, Playa, C.P. 12200, Ciudad de la Habana, Cuba {rabella, jmedina} @cenatav.co.cu Abstract. This paper describes a new method, called IClustSeg, for linear text segmentation by topic using an incremental overlapped clustering algorithm. Incremental algorithms are able to process new objects as they are added to the collection and, according to the changes, to update the results using previous information. In our approach, we maintain a structure to get an incremental overlapped clustering. The results of the clustering algorithm, when processing a stream, are used any time text segmentation is required, using the clustering cohesion as the criteria for segmenting by topic. We compare our proposal against the best known methods, outperforming significantly these algorithms. 1 Introduction Topic segmentation intends to identify the boundaries in a document with goal of capturing the latent topical structure. The automatic detection of appropriate subtopic boundaries in a document is a very useful task in text processing. For example, in information retrieval and in passages retrieval, to return documents, segments or passages closer to the user’s queries. Another application of topic segmentation is in summarization, where it can be used to select segments of texts containing the main ideas for the summary requested [6]. Many text segmentation methods by topics have been proposed recently. Usually, they obtain linear segmentations, where the output is a document divided into sequences of adjacent segments [7], [9].
    [Show full text]
  • A Text Denormalization Algorithm Producing Training Data for Text Segmentation
    A Text Denormalization Algorithm Producing Training Data for Text Segmentation Kilian Evang Valerio Basile University of Groningen University of Groningen [email protected] [email protected] Johan Bos University of Groningen [email protected] As a first step of processing, text often has to be split into sentences and tokens. We call this process segmentation. It is often desirable to replace rule-based segmentation tools with statistical ones that can learn from examples provided by human annotators who fix the machine's mistakes. Such a statistical segmentation system is presented in Evang et al. (2013). As training data, the system requires the original raw text as well as information about the boundaries between tokens and sentences within this raw text. Although raw as well as segmented versions of text corpora are available for many languages, this required information is often not trivial to obtain because the segmented version differs from the raw one also in other respects. For example, punctuation marks and diacritics have been normalized to canonical forms by human annotators or by rule-based segmentation and normalization tools. This is the case with e.g. the Penn Treebank, the Dutch Twente News Corpus and the Italian PAISA` corpus. This problem of missing alignment between raw and segmented text is also noted by Dridan and Oepen (2012). We present a heuristic algorithm that recovers the alignment and thereby produces standoff annotations marking token and sentence boundaries in the raw test. The algorithm is based on the Levenshtein algorithm and is general in that it does not assume any language-specific normalization conventions.
    [Show full text]
  • Topic Segmentation: Algorithms and Applications
    University of Pennsylvania ScholarlyCommons IRCS Technical Reports Series Institute for Research in Cognitive Science 8-1-1998 Topic Segmentation: Algorithms And Applications Jeffrey C. Reynar University of Pennsylvania Follow this and additional works at: https://repository.upenn.edu/ircs_reports Part of the Databases and Information Systems Commons Reynar, Jeffrey C., "Topic Segmentation: Algorithms And Applications" (1998). IRCS Technical Reports Series. 66. https://repository.upenn.edu/ircs_reports/66 University of Pennsylvania Institute for Research in Cognitive Science Technical Report No. IRCS-98-21. This paper is posted at ScholarlyCommons. https://repository.upenn.edu/ircs_reports/66 For more information, please contact [email protected]. Topic Segmentation: Algorithms And Applications Abstract Most documents are aboutmore than one subject, but the majority of natural language processing algorithms and information retrieval techniques implicitly assume that every document has just one topic. The work described herein is about clues which mark shifts to new topics, algorithms for identifying topic boundaries and the uses of such boundaries once identified. A number of topic shift indicators have been proposed in the literature. We review these features, suggest several new ones and test most of them in implemented topic segmentation algorithms. Hints about topic boundaries include repetitions of character sequences, patterns of word and word n-gram repetition, word frequency, the presence of cue words and phrases and the use of synonyms. The algorithms we present use cues singly or in combination to identify topic shifts in several kinds of documents. One algorithm tracks compression performance, which is an indicator of topic shift because self-similarity within topic segments should be greater than between-segment similarity.
    [Show full text]
  • A Generic Neural Text Segmentation Model with Pointer Network
    Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18) SEGBOT: A Generic Neural Text Segmentation Model with Pointer Network Jing Li, Aixin Sun and Shafiq Joty School of Computer Science and Engineering, Nanyang Technological University, Singapore [email protected], {axsun,srjoty}@ntu.edu.sg Abstract [A person]EDU [who never made a mistake]EDU [never tried any- thing new]EDU Text segmentation is a fundamental task in natu- ral language processing that comes in two levels of Figure 1: A sentence with three elementary discourse units (EDUs). granularity: (i) segmenting a document into a se- and Thompson, 1988]. quence of topical segments (topic segmentation), Both topic and EDU segmentation tasks have received a and (ii) segmenting a sentence into a sequence of lot of attention in the past due to their utility in many NLP elementary discourse units (EDU segmentation). tasks. Although related, these two tasks have been addressed Traditional solutions to the two tasks heavily rely separately with different sets of approaches. Both supervised on carefully designed features. The recently propo- and unsupervised methods have been proposed for topic seg- sed neural models do not need manual feature en- mentation. Unsupervised topic segmentation models exploit gineering, but they either suffer from sparse boun- the strong correlation between topic and lexical usage, and dary tags or they cannot well handle the issue of can be broadly categorized into two classes: similarity-based variable size output vocabulary. We propose a ge- models and probabilistic generative models. The similarity- neric end-to-end segmentation model called SEG- based models are based on the key intuition that sentences BOT.SEGBOT uses a bidirectional recurrent neural in a segment are more similar to each other than to senten- network to encode input text sequence.
    [Show full text]
  • Text Segmentation Techniques: a Critical Review
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Sunway Institutional Repository Text Segmentation Techniques: A Critical Review Irina Pak and Phoey Lee Teh Department of Computing and Information Systems, Sunway University, Bandar Sunway, Malaysia [email protected], [email protected] Abstract Text segmentation is widely used for processing text. It is a method of splitting a document into smaller parts, which is usually called segments. Each segment has its relevant meaning. Those segments categorized as word, sentence, topic, phrase or any information unit depending on the task of the text analysis. This study presents various reasons of usage of text segmentation for different analyzing approaches. We categorized the types of documents and languages used. The main contribution of this study includes a summarization of 50 research papers and an illustration of past decade (January 2007- January 2017)’s of research that applied text segmentation as their main approach for analysing text. Results revealed the popularity of using text segmentation in different languages. Besides that, the “word” seems to be the most practical and usable segment, as it is the smaller unit than the phrase, sentence or line. 1 Introduction Text segmentation is process of extracting coherent blocks of text [1]. The segment referred as “segment boundary” [2] or passage [3]. Another two studies referred segment as subtopic [4] and region of interest [5]. There are many reasons why the splitting document can be useful for text analysis. One of the main reasons is because they are smaller and more coherent than whole documents [3].
    [Show full text]
  • Text Segmentation Based on Semantic Word Embeddings
    Text Segmentation based on Semantic Word Embeddings Alexander A Alemi Paul Ginsparg Dept of Physics Depts of Physics and Information Science Cornell University Cornell University [email protected] [email protected] ABSTRACT early algorithm in this class was Choi's C99 algorithm [3] We explore the use of semantic word embeddings [14, 16, in 2000, which also introduced a benchmark segmentation 12] in text segmentation algorithms, including the C99 seg- dataset used by subsequent work. Instead of looking only at nearest neighbor coherence, the C99 algorithm computes mentation algorithm [3, 4] and new algorithms inspired by 1 the distributed word vector representation. By developing a coherence score between all pairs of elements of text, a general framework for discussing a class of segmentation and searches for a text segmentation that optimizes an ob- objectives, we study the effectiveness of greedy versus ex- jective based on that scoring by greedily making a succes- act optimization approaches and suggest a new iterative re- sion of best cuts. Later work by Choi and collaborators finement technique for improving the performance of greedy [4] used distributed representations of words rather than a strategies. We compare our results to known benchmarks bag of words approach, with the representations generated [18, 15, 3, 4], using known metrics [2, 17]. We demonstrate by LSA [8]. In 2001, Utiyama and Ishahara introduced a state-of-the-art performance for an untrained method with statistical model for segmentation and optimized a poste- our Content Vector Segmentation (CVS) on the Choi test rior for the segment boundaries. Moving beyond the greedy set.
    [Show full text]
  • Word Sense Disambiguation and Text Segmentation Based on Lexical
    Word Sense i)ismnl}iguati<)n and Text Set mentation Bas(q on I,c×ical (+ohcslOll ()KUMUI{A Manabu, IIONI)A Takeo School of [nforma,tion Science, Japan Advanced Institute of Science a.nd Technology ('l'al.sunokuchi, lshikawa 923-12 Japan) c-nmil: { oku,honda}¢~jaist.ac.ji~ Abstract cohesion is far easier to idenlAfy than reference be- cause 1)oth words in lexical cohesion relation ap- In this paper, we describe ihow word sense am= pear in a text while one word in reference relation biguity can be resolw'.d with the aid of lexical eo- is a pr<mom, or elided and has less information to hesion. By checking ]exical coheshm between the infer the other word in the relation automatically. current word and lexical chains in the order of Based on this observation, we use lexical cohe- the salience, in tandem with getmration of lexica] sion as a linguistic device for discourse analysis. chains~ we realize incretnental word sense disam We call a sequence of words which are in lexieal biguation based on contextual infl)rmation that cohesion relation with each other a Icxical chain lexical chains,reveah Next;, we <le~<:ribe how set like [10]. l,exical chains tend to indicate portions men< boundaries of a text can be determined with of a text; that form a semantic uttit. And so vari.- the aid of lexical cohesion. Wc can measure the ous lexical chains tend to appear in a text corre. plausibility of each point in the text as a segment spou(ling to the change of the topic.
    [Show full text]
  • Multiple Segmentations of Thai Sentences for Neural Machine
    Multiple Segmentations of Thai Sentences for Neural Machine Translation Alberto Poncelas1, Wichaya Pidchamook2, Chao-Hong Liu3, James Hadley4, Andy Way1 1ADAPT Centre, School of Computing, Dublin City University, Ireland 2SALIS, Dublin City University, Ireland 3Iconic Translation Machines 4Trinity Centre for Literary and Cultural Translation, Trinity College Dublin, Ireland {alberto.poncelas, andy.way}@adaptcentre.ie [email protected], [email protected], [email protected] Abstract Thai is a low-resource language, so it is often the case that data is not available in sufficient quantities to train an Neural Machine Translation (NMT) model which perform to a high level of quality. In addition, the Thai script does not use white spaces to delimit the boundaries between words, which adds more complexity when building sequence to sequence models. In this work, we explore how to augment a set of English–Thai parallel data by replicating sentence-pairs with different word segmentation methods on Thai, as training data for NMT model training. Using different merge operations of Byte Pair Encoding, different segmentations of Thai sentences can be obtained. The experiments show that combining these datasets, performance is improved for NMT models trained with a dataset that has been split using a supervised splitting tool. Keywords: Machine Translation, Word Segmentation, Thai Language In Machine Translation (MT), low-resource languages are sentence into words (or tokens). We investigate three split- especially challenging as the amount of parallel data avail- ting strategies: (i) Character-based, using each character as able to train models may not be enough to achieve high token. This is the simplest approach as there is no need to translation quality.
    [Show full text]
  • A Journey Through Existing Procedures on Ocr Recognition and Text
    A JOURNEY THROUGH EXISTING PROCEDURES ON OCR RECOGNITION AND TEXT TO SPEECH CONVERSION PROCESS OF BILINGUAL LANGUAGES K.Shanmugam1, Dr.B.vanathi2 1Assistant Professor,2Professor, 1,2Department of Computer Science and Engineering Valliammai Engineering College, Chennai,Tamilnadu ABSTRACT Microcontroller to hear the spoken words of the In this paper we try to explore various text through the earphone or speaker plugged Character recognition and Text-to-Speech into it through its audio jack. synthesis techniques developed and OCR is one of the challenging areas of character implemented by several researchers and recognition and it has various practical research groups over the world during the last applications. decades. Various existing proposals focus on A Text-To-Speech (TTS) synthesizer is a the implementation of either OCR or Text to computer-based system that should be able to Speech synthesis. Text segmentation and read any text aloud, whether it was directly recognition of Tamil characters poses a new introduced in the computer by an operator or challenge owing to its large number of scanned and submitted to an Optical Character characters, curves, indefinite shapes; Recognition (OCR) system. techniques that recognise such text are The recognised text is converted into discussed. This paper aims to give an overview speech by using text to speech synthesizer. of OCR and text synthesis for Bilingual languages (English and Tamil languages), summarizes and compares the characteristics of various synthesis techniques used. KEYWORDS: Optical character recognition, Text-To- Speech (TTS) synthesizer, Win32 SAPI. INTRODUCTION: Machine replication of human functions like reading, is an ancient dream. However, over the last five decades, machine reading has grown from dream to reality.
    [Show full text]