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.