A Survey Paper on Optical Character Recognition using Machine Learning

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1 A Survey Paper on Optical Character Recognition using Machine Learning 1 Vishal Chourasia, 2 Dr. Sanjay Silakari, 3 Dr. Rajeev Pandey 1 vishalchourasia128@gmail.com, 3 rajeev98iet@gmail.com Department of Computer Science Engineering, UIT RGPV, Bhopal (India) Abstract: In the arena of computer science, there is promising ultimatum for the software systems to separate characters in computer system when information is perused through paper documents. As we realized that numerous newspapers and books which are distributed in many layouts related to different subjects. The main persistence of Optical Character Recognition (OCR) method is to achieve Document Analysis. This intentions to improve the exactness of identifying the fonts during manuscript processing compared to various current recognition techniques. In this survey paper, OCR technique develops the characters from the bit-mapped pictures. The main goal is to successfully concentrate all the text present in an image and convert it to machine readable format. Keywords: Character Recognition, Image Processing, Machine Learning. I. INTRODUCTION Nowadays there is a colossal supplication in "putting away the data presented in the paper records into a PC memory and later rescuing this data by sharp process". The best approach to accumulate information in these paper records into PC framework is to first scan the archives and afterward store them as pictures. Be that as it may, to reuse this information it is exceptionally risky to peruse the particular substance and looking through the contents these reports line-by-line in addition word-by word. The explanation behind this difficulty is the text style attributes of the characters in paper archives are diverse to the textual style of the characters in PC frameworks. Therefore, the PC can't perceive the characters while understanding them. This idea of putting away the contents of paper archives in PC storage place and after that reading and looking through the contents is called document processing. Now and again in this document processing, we have to route the information that is identified with dialects other than the English on the world. For the report processing, we require a product framework called character recognition system; this procedure is likewise called document image analysis (DIA). Along these lines our necessity is to improve character recognition software method to complete Document Image Analysis which changes documents in paper format to electronic format. For this procedure there are different strategies in the world. Among each one of those strategies we need to pick Optical Character Recognition as principle key procedure to perceive characters. The change of paper archives in to electronic format is an on-going undertaking in a considerable lot of the associations especially in Research and Development (R&D) zone, in huge business enterprise, in government establishments, so on. From the problem statement, we can present the need of Optical Character Recognition in versatile electronic gadgets, for example, mobile phones, computerized cameras to procure pictures and remember them as a piece of face recognition and validation. II. LITERATURE REVIEW In the research paper [1], Tao Wang et. al. proposed Full end-to-end content recognition in regular images is a testing issue that has gotten much attention as of late. Customary systems around there have depended on expound models fusing thoroughly hand built highlights or a lot 160

2 of prior learning. In this manuscript, they take an alternate route and join the authentic power of substantial, multilayer neural networks together with late improvements in unsupervised feature learning, which enables us to utilize a typical framework to prepare highly precise text detector and character recognizer modules. At that point, utilizing just straightforward off-therack methods, we integrate these two modules into a full end-to-end, lexicon-driven, scene text recognition structure that achieves state-of-theart presentation on standard benchmarks, specifically Street View Text and ICDAR In the paper High-for-Low and Low-for-High: Efficient Boundary Detection from Deep Object Features and its Applications to High-Level Vision [2] the greater part of the present boundary detection systems depend solely on low-level highlights, for example, color and texture. Be that as it may, recognition examines propose that people utilize object level reasoning when judging if a specific pixel is a boundary. Roused by this perception, in this work they demonstrate to foresee boundaries by misusing object level features from a pre-prepared object classification network. Our strategy can be seen as a High-for-Low approach where high level objects features advice the low-level boundary detection process. Our model accomplishes best in class execution on a built up boundary detection benchmark and it is productive to run. In the paper, Text Detection and Character Recognition in Scene Images with Unsupervised Feature Learning [3] author proposed perusing content from photos is a challenging issue that has gotten a lot of consideration. Two key parts of most system are (i) text detection from pictures and (ii) character recognition, and numerous ongoing methods have been proposed to configuration better component portrayals and models for both. In this paper, we apply techniques as of late created in machine learning particularly, large scale algorithms for learning the features consequently from unlabeled data and demonstrate that they allow us to develop profoundly effective classifiers for both detection and recognition to be utilized as a part of a high accuracy end-to-end system. Unsupervised Visual Representation Learning by Context Prediction [4] this work explores the utilization of spatial context as a source of free and abundant supervisory flag for training a rich visual representation. Given only an extensive, unlabeled image collection, we separate random sets of patches from each picture and train a convolution neural net to predict the position of the second patch with respect to the first. We contend that doing well on this task requires the model to figure out how to recognize objects and their parts. We show that the feature representation got the hang of utilizing this inside picture setting for sure catches visual similarity crosswise over pictures. For instance, this representation enables us to perform unsupervised visual discovery of objects like cats, people, and even birds from the Pascal VOC 2011 detection dataset. A Deep Visual Correspondence Embedding Model for Stereo Matching in this paper [5] author presents a data-driven matching cost for stereo matching. A novel deep visual correspondence embedding model is prepared by means of Convolutional Neural Network on a substantial arrangement of stereo images with round truth disparities. This deep embedding model use appearance information to learn visual similarity connections between comparing picture patches, and explicitly maps intensity values into an implanting feature space to quantify pixel dissimilarities. Exploratory results on KITTI and Middlebury data sets show the adequacy of our model. In the first place, we demonstrate that the new measure of pixel difference beats customary coordinating costs. Moreover, when generated with a worldwide stereo system, our strategy positions top 3 among every one of the two-outline calculations on the KITTI benchmark. At long last, crossapproval comes about demonstrate that our model can make rectify forecasts for concealed information which are outside of its named labeled set [7]. Performance Evaluation of preventives Edge Detector for Noisy Images [6] author proposed the edge detection is at the front line of image processing for object detection, it is crucial to have a decent comprehension of edge detection 161

3 algorithm. This paper assesses the execution of Prewitt Edge Detector for detection of edge in advanced image debased with various types of noise. Various types of noise are contemplated with a specific end goal to assess the performance of the Prewitt Edge Detector. Further, the different standard test pictures are inspected to validate our outcomes [6]. 2D Basic Shape Detection using Region Properties in this paper author proposed basic 2D object Detection alludes to distinguish an location that recognizes and register components of an area that distinguish the components of a specific object class at different levels of details. Image Processing Algorithm is fundamental for image PC analysis and machine vision. The objective of the essential 2D object recognition system is to distinguish the fundamental geometric shape of objects introduce in the picture. It utilizes some picture handling calculation and strategies to distinguish objects from the picture and contrast it and the property of fundamental geometric shape to order what protest is like which specific geometric shape like circle, triangle, square and square shape. The discovery of edges of items from a picture is finished by utilizing edge location system [7] Modified Shape Prediction algorithm using over segmentation [8] this paper represents to a changed shape prediction calculation to anticipate the state of various articles utilizing over division strategy. This is the principle look into point to anticipate the state of various objects. Highlight extraction process is utilized as a part of it for foreseeing the states of question. Before include extraction process, right off the bat foresee the limit of the shape. In view of morphological orders highlights of the articles are extraction utilizing fuzzy logic operations. After the element extraction process, at that point utilize the expectation table for coordinating the element of the present protest with the preloaded include information based or preparing set. This calculation predicts the distinctive state of articles relying upon two parameters corners, and the measurements (length, breadth) of a specific protest in a forecast table [8]. In the research work of Edge Based Region Growing [9], Image segmentation is a disintegration of scene into its parts. It is enter advance in picture examination. Edge, point, line, limit, surface and area identification are the different structures and locale discovery are the different types of picture division. Two of the primary picture division system edge discovery and area developing are exceptionally being used for picture division [9]. III. PREVAILING SYSTEM In the running domain, there is a developing demand for the clients to change over the printed records into electronic reports for keeping up the security of their information. Subsequently the essential OCR system was designed to change over the information accessible on papers into PC process able archives, So that the records can be editable and returnable. The existing system deals with homogenous character recognition and also it deals with character recognition of printed text. Drawback of existing system encounters two kinds of problems: 1. It cannot provide high accuracy with handwritten text. 2. It cannot deal with cursive text, i.e. it encounters problem when text is tilted at some angle. IV. PROPOSED SYSTEM The basic process of Optical Character Recognition includes four main stages: 1. Image Acquisition and Pre-processing: Image to be acquired using any available hardware. These captured images are in RGB format. Pre-processing is essential to enhance the input image which in turn reduces time complexity for localization and segmentation of characters. It mainly involves series of filtering and saturating the image to make the required region prominent. Finally, it involves converting the image into gray scale and increasing the contrast. This step does not involve any 162

4 learning algorithm and can be done easily using any language. 2. Text localization: In this stage, the location of all the text areas is identified and the output of this stage will be sub-images that contain only the text. This is done by using a sliding window whose size can vary. This window will move over the image and a classifier will classify each position of the window as Contains text or Doesn t contain text It involves two main steps. a. Locating a large bounding rectangle over the all the text regions. b. Determining the exact location of the text. 3. Character Segmentation: This stage is meant for segmentation of the characters from images obtained from the previous step. The specific characters have to be illustrious (segmented) from apiece other, this is done by using a 1-D window with height as much as the height of the text and sliding it over each text so as to segment the text into characters. Another classification algorithm is used at this stage to classify each window position as split or no split. The images will be split at the middle of the windows which are classified as split. The output of this stage is a set of monochrome images for each character in the plate. 4. Character Recognition: Character recognition step will be classifying the characteristics of the character input photo. In this phase, the portioned characters are rescaled to coordinate the characters into a standard size. And then they will be classified using a machine learning algorithm, say one-vs-all SVM or a neural network. V. CONCLUSION What does the upcoming hold for OCR? Sufficiently given entrepreneurial designer and adequate innovative improvement dollars, OCR can become into an intense tool for future information entry applications. Be that as it may, the limited accessibility of assets in a capitalshort condition could confine the development of this technology. But, given the best possible impetus and encouragement, a great deal of advantages can be given by the OCR scheme. They are: 1. The computerized passage of information by OCR is a standout amongst the most appealing, works diminishing innovation. 2. The acknowledgment of new text style characters by the framework is simple and brisk. 3. We can alter the data of the reports all the more advantageously and we can reuse the altered data as and when required. 4. The extension to programming other than altering and searching is a theme down future works. The Grid framework utilized as a part of the execution of Optical Character Recognition system can be proficiently used to accelerate the interpretation of picture based records into organized reports that are presently simple to find, discover and process. REFERENCES [1] Tao Wang, David J. Wu, Adam Coates, Andrew Y. Ng, "End-to-End Text Recognition with Convolutional Neural Networks" Stanford University, CA [2] Gedas Bertasius, Jianbo Shi, Lorenzo Torresani "High-for-Low and Low-for-High: Efficient Boundary Detection from Deep Object Features and its Applications to High-Level Vision" University of Pennsylvania. [3] Adam Coates, Blake Carpenter, Carl Case, Sanjeev Satheesh, Bipin Suresh, Tao Wang, David J. Wu, Andrew Y. Ng, "Text Detection and Character Recognition in Scene Images with Unsupervised Feature Learning" Computer Science Department, Stanford University, USA [4] Carl Doers, Abhinav Gupta, Alexei A. Efros, "Unsupervised Visual Representation Learning by Context Prediction", School of Computer Science and Dept. of Electrical Engineering and Computer Science, Carnegie Mellon University of California, Berkeley. 163

5 [5] Zhuoyuan Chen, Xun Sun, Liang Wang, "A Deep Visual Correspondence Embedding Model for Stereo Matching Costs", Baidu Research Institute of Deep Learning. [6] Raman Maini, "Performance Evaluation of preventives Edge Detector for Noisy Images", J.S. Shell University college of Engineering, Punjab University, Patiala. [7] Ghanshyam I. Prajapati, "2D Basic Shape Detection using Region Properties", Dept of Information Technology SVMIT Engineering College Baruch, Gujurat. [8] Harpeet Kaur, "Modified Shape Prediction algorithm using Over segmentation", Chandigarh Engineering Collage, Mohali. [9] Rupinder Singh et al, "Edge Based Region Growing", International Joural of Computer Technology and Applications, Vol (2) 164