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May 2018 | Vol. 1 Issue. 1
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A SURVEY ON DEEP LEARNING METHOD USED FOR
CHARACTER RE...
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documents on a computer storage disk and then
reuse th...
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preprocessing. Convolutional Neural Networks
(CNN) als...
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the middle, half way between value of all the bit of
p...
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classifier takes the feature vector as input and
perfo...
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They are going to take the MNIST dataset
for training ...
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[1] Ema Rachmawati, Masayu Leylia khodra, Iping
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The field of Artificial Intelligence is very fashionable today, especially neural networks that work well in various areas such as speech recognition and natural language processing. This Research Article briefly describes how deep learning models work and what different techniques are used in text recognition. It also describes the great progress that has been made in the field of medicine, the analysis of forensic documents, the recognition of license plates, banking, health and the legal industry. The recognition of handwritten characters is one of the research areas in the field of artificial intelligence. The individual character recognition has a higher recognition accuracy than the complete word recognition. The new method for categorizing Freeman strings is presented using four connectivity events and eight connectivity events with a deep learning approach.

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A SURVEY ON DEEP LEARNING METHOD USED FOR CHARACTER RECOGNITION

  1. 1. © IJCIRAS May 2018 | Vol. 1 Issue. 1 IJCIRAS1004 WWW.IJCIRAS.COM 14 A SURVEY ON DEEP LEARNING METHOD USED FOR CHARACTER RECOGNITION Aditi M Joshi1 , Ashish D Patel2 1 Information Technology Department, Gujarat Technological University, Ahmedabad 2 Professor, Computer Engineering Department, Gujarat Technology University, Ahmedabad Abstract The field of Artificial Intelligence is very fashionable today, especially neural networks that work well in various areas such as speech recognition and natural language processing. This Research Article briefly describes how deep learning models work and what different techniques are used in text recognition. It also describes the great progress that has been made in the field of medicine, the analysis of forensic documents, the recognition of license plates, banking, health and the legal industry. The recognition of handwritten characters is one of the research areas in the field of artificial intelligence. The individual character recognition has a higher recognition accuracy than the complete word recognition. The new method for categorizing Freeman strings is presented using four connectivity events and eight connectivity events with a deep learning approach. Keyword: deep learning, image classification; Number recognition, Shape Based Recognition, (chain-code Biased) Hub, Hidden Markov Models(HMM), Pattern recognition, Freeman chain code, Image processing, convolutional neural networks, Deep convolutional neural networks, Power Quality. 1.INTRODUCTION Deep learning has become the most popular approach to develop Artificial Intelligence (AI) machines that perceive and understand the world. Here is an overview of deep learning methods for image classification and number recognition in images. Deep Learning uses neural networks that transmit data across multiple processing layers to interpret the properties and relationships of the data. Advanced learning algorithms are largely self- centered in data analysis as they go into production. Researchers have experienced many ups and downs while early work on artificial neural networks has always been of particular interest to researchers. Neural network-based methods have been successfully applied t`o classification, clustering, recognition, approximation, forecasting and problems in medicine, biology, commerce, robotics, and so forth. The latest advances in this area have been made through the invention of advanced learning methods. Hardware and software for parallel computing. For processing numerical data, the human brain is so sophisticated that we recognize objects in a few seconds, without much difficulty. Computer vision is more ambitious. It tries to mimic the human visual system and is often associated with scene understanding. Most image processing algorithms produce results that can serve as the first input for machine vision algorithms. Image processing is a logical extension of signal processing. When an unknown animal is encountered, we try to recognize it by comparing its features (called patterns) with known stored patterns that we already have. This process of comparing an unknown object with stored patterns to recognize the unknown object is called classification. Thus, classification is the process of applying a label or pattern class to unknown instance. In the absence of any prior knowledge of the object or stored pattern, we use a trial and error process to recognize the object. This trial and error process of grouping of objects is called clustering. Most organizations consult handwritten documents such as forms, checks, etc. The manuscript documents are converted and stored in digital formats for easy retrieval. Without a handwritten character recognition software, the source would require the employment of dedicated employees to convert the handwritten document into a digital format by manually entering the text. Today it is very important to store the information available in paper
  2. 2. © IJCIRAS May 2018 | Vol. 1 Issue. 1 IJCIRAS1004 WWW.IJCIRAS.COM 15 documents on a computer storage disk and then reuse this information by doing a search. One way to store information from paper documents in the computer system is to scan the document. But the scanned image cannot be changed by the user. The character recognition technique is used to identify handwritten characters. For example, Guess the name of the prescribed drug and does not give the exact name because the doctors write. The recognition of handwritten characters can be classified as recognition of online spelling and offline writing. The recognition of online spelling involves the automatic conversion of text as written on a special digitizer or PDA, in which a sensor captures pen movements and pen / pen switching. This type of data is known as digital ink or digital representation of handwriting. Offline The character of the manuscript implies, from the image, the automatic conversion of text into literal codes that can be used in computer applications and other word processing applications. Figure 1 Types of Handwritten Character Recognition Handwritten character recognition system includes all processing segment like pre-processing, separation(Segmentation), feature mining(Extraction), training and testing and post processing. Figure 2 Steps for Recognition Freeman Chain Code (FCC) introduced by Freeman, 1961, The development and improvement of the chain code illustration system has been broadly castoff as a research topic [9]. Freeman codes or chain code are used to represent the boundary of the image. There are two types of chain codes corresponding to 4-neighbourhoods and 8- neighbourhoods. The boundary can be traced and allotted numbers based on the directions. Freeman chain code is the best proven method for number recognition. Figure 3 Image chain code a) 4- Directional code b) 8 – Directional code The elementary principle of chain codes is to disjointedly encode each linked component in the image where chains characterize the edge of the objects. Neural networks also identify models with extreme changeability (such as handwritten characters Reorganization) and robustness to simple distortions and geometric transformations. Generating rules in neural networks is not a direct process. The inputs of the neural networks must be digital. Other types of data must first be converted into a number. Figure 4 Architecture of Convolutional Neural Networks Convolutional neural networks (CNNs) are a different(Special) type of multilayered neural networks designed to distinguish visual patterns reliably from pixel images with nominal
  3. 3. © IJCIRAS May 2018 | Vol. 1 Issue. 1 IJCIRAS1004 WWW.IJCIRAS.COM 16 preprocessing. Convolutional Neural Networks (CNN) also used for speech recognition. Deep Convolutional Neural Network (CNN) have shown superior results to traditional shallow networks in many recognition tasks [4]. Deep Neural Networks do not require any feature to be explicitly defined, they work on the raw pixel data generating the best features and using them to classify the inputs into different classes. Neural network are powerful to handle noisy data and enhance the performance continuedly. They have low error frequency and high precision value. LeNet-5 was designed by Lecun et al and is the latest convolutional network designed for handwritten and machine-printed character recognition. Lenet – 5 has 7 layers (2 Convolutional layers, 2 Subsampling layers, 2 hidden layers and 1 output layer) and not work on large images. It is one of the first shallow Convolutional neural network specifically designed to classify handwritten digit. It is trained and tested on the MNIST data set to classify the input into one of the ten classes representing 0-9 digits. 2. STRUCTURE OF CHARACTER RECOGNITION SYSTEM The character recognition system is the process of recognizing the character of the image of the printed document or the image of the manuscript document. The main problem in recognizing handwritten characters is the variety of writing styles, which varies from one author to another. The schematic diagram of the character recognition system is shown in FIG 5. Classification is the supervised learning method. In the training phase, the classifier algorithm is fed by a large amount of known data. This record is called training data or labeled data. Figure 5 Block diagram of Character Recognition System [7]. The training data should generally large and representative in nature. Collected datasets are divided in to two parts Training data and testing data. For train the system training data are used and this trained classification further used to distinguish test data. Generally, the performance of the classifier depends on factors such as the nature of data and the nature of learning. 2.1 Preprocessing This step involves a basic processing of the image before it is used for recognition by the system. It has to be processed in such a way that it is suitable for the system to understand. The steps involved are: Noise removal There are several reasons due to which noise gets added in the images. Noise reduction is the method to eliminate noise from the scanned image with an appropriate filter. Smoothing is used to reduce noise and remove small details from the image by removing large objects. It could be from the mechanism that is used to acquire the image, the film grain or the electronic transmission of the image. The noise can be removed by linear filtering, median filtering or adaptive filtering. Binarization Binarization is process of converting from a gray scale image to a binary image using the global thresholding technique such as Otsu’s method of counting threshold. Otsu’s method serves optimum threshold value. Binarization is a process where each bit of picture in the image is got changed into one bit and given to a value of 0 or 1 depending upon
  4. 4. © IJCIRAS May 2018 | Vol. 1 Issue. 1 IJCIRAS1004 WWW.IJCIRAS.COM 17 the middle, half way between value of all the bit of picture. Figure 6 Steps for preprocessing [8]. Edge Detection Edges play a very significant role in many image processing purposes. They give an overview of the object. In the physical plane, the comparability of the edges in the depth thresholds, the position of the surface, the properties of the materials and the differences in brightness change. An edge is a set of grouped pixels that lie on the boundary between two regions that differ in gray scale. The pixels on an edge are called edge points. When the edge is detected, unnecessary details are removed and only important structural information is retained. Normally, an edge is extracted by calculating the derivative of the image function. Dilation and filling The basic effect of using the expansion operator in a binary image is to progressively increase the limit of areas of pixels in the foreground. As a result, areas of foreground pixels increase in size as the holes in those areas become smaller. Processed image for feature extraction This step extracts the properties of the characters that are critical to the classification in the detection phase. This step is important because its efficient operation improves the detection rate and reduces misclassification. 2.2 Segmentation Image segmentation has developed as an imperative phase in image application area. Segmentation breaks the words or sentences in to pieces such that a clear boundary is set between the characters. Segmentation is the process of dividing an image into multiple regions and mining a significant region known as a region of interest.Region of interest (ROI) may vary with applications. For example, if the goal of doctor is to analyze the tumor in a computer tomography (CT) image, then the tumor in the image is the ROI. Similarity, if the image application aims to recognize the iris in eye image, then the iris in the eye image is the required ROI. Segmentation includes line segmentation, word segmentation, and character segmentation. If line segmentation is the separation of a line from the paragraph, word segmentation is a separation of the word from the line, and character segmentation is the separation of characters and words. Image segmentation algorithms are created using the principle of discontinuity or the principle of similarity. The central idea of the discontinuity principle extracts the areas that contrast in properties such as color, intensity or any other image statistic. Figure 7 Segmentation free and segmentation based method [7]. 2.3. Feature Extraction Image pre-processing and feature extraction techniques are mandatory for any applications that is based on image. Feature Extraction is a process of extraction and generation of features to assist the task of object classification. This phase is critical because the quality of the features influences the classification task. The extended set of features is stored as a vector called as the feature vector. The
  5. 5. © IJCIRAS May 2018 | Vol. 1 Issue. 1 IJCIRAS1004 WWW.IJCIRAS.COM 18 classifier takes the feature vector as input and performs the classification. 2.4. Classification The classification phase is the decision-making part of a recognition system and uses the features extracted in the previous step. The feature vector is indicated by X, where X = (f1, f2, ..., fd), where f indicates the properties and d denotes the number n. feature extracted from the character Due to the comparison of characteristics, vector feature are classified and recognized efficiently in the corresponding class. 2.5.Post – processing At this point, the accuracy of character recognition is further increased by linking the dictionary to the system to perform a syntactic analysis, a sort of semantic analysis of the main concepts that is used to verify the recognized character. This step is not required in the character recognition system. 3. Related Work The recognition of the characters of the image depends on the compassion of the selected entities and the type of classifier used to identify the image.Following are the papers which perform character recognition with different algorithms and they also used different deep learning approaches for better accuracy and recognition. Freeman chain coding is used to create contour of the object in the image, which is than proceeds further for finding modified set of Fragments. For the purpose of scoring functions Modified Needleman – Wunsch algorithm is used [1]. Rounding number calculations with Hub formats. This is well-suited for Digital circuit equations. This paper has suggested the use of solving complex numerical issues for binary and numeric values processing [2]. Power quality waveforms are used while the image characters are wavy in format. In this papers possible PQ waveform deviation are found from internet. For this, previously recorded data and correct identification of current data is performed using signals. Google engine search engine tool perform its process with this format [3]. They introduce a new public image dataset for Devanagari script: Devanagari Handwritten Character Dataset (DHCD). In Devanagari script, the base form of consonant characters can be combined with vowels to form additional characters which is not explored in this research. Deep neural network was trained on the DHCD as a multi-class classification problem. Devanagari characters are least explored for image processing. Handwritten and printed both have different way to recognize them. Deep learning with CNN and multilayer perception network is used in this paper for accurate recognition of character [4]. Hidden markov model in machine learning is used to recognize the series of states from a series of observations. For example: We don't get to observe the actual order of states. Rather, we can only examine some outcome generated by each state. There can be Viterbi or Baysen model to find the maximum likelihood state assignment. Pattern of Devanagari is explored with hmm and Ann in this case to find out results of character recognition from an online isolated character set of devanagari [5]. Hidden markov display in machine learning is utilized to perceive the successions of states from a progression of perceptions. For instance: We don't get the chance to watch the genuine order of states. or maybe, we can just analyze some result created by each state. There can be Viterbi or Baysen model to locate the most extreme probability state assignment. Example of Devanagari is investigated with HMM and ANN for this situation to discover consequences of character recognition from an online isolated character set of devanagari [5]. To process the images and characters from cheques, this paper has used k-space auto encoders. and the hidden layers of auto encoders are found with CNN for single digit analyzer. They have done modification to perform soft-tuning of the weights of each stacked layer separately from other hidden layers. Further it can be improved to find modified process to improve the feature extraction process and representation efficiency [6].
  6. 6. © IJCIRAS May 2018 | Vol. 1 Issue. 1 IJCIRAS1004 WWW.IJCIRAS.COM 19 They are going to take the MNIST dataset for training and recognition. The primary aim of this dataset is to classify the handwritten digits 0-9. They have a total of 70,000 images for training and testing. Each digit is represented as a 28 by 28 grey scale pixel intensity for better results. The digits are passed into input layers of LeNet and then into the hidden layers which contain two sets of convolutional, activation and pooling layers. Then finally it is mapped onto the fully connected layer and given a softmax classifier to classify the digits. They are going to implement this network using keras deep learning inbuilt python library [12]. The chain code discriptor string code descriptor was calculated and tested with a completely different classifier such as k-Neighbor Neighbor (k-NN), Support Vector Machine (SVM), and Naive Thomas Bayes. Principal Component Analysis (PCA) was used in the description of the string code [14]. 4. COMPARISION A Convolutional network has a benefit over other Artificial Neural networks in extracting and utilizing the features data, enhancing the knowledge of 2D shapes with higher degree of accuracy and unvarying to translation, scaling and other distortions [12]. Histogram of Oriented Gradients (HOG) is best algorithm for feature extraction and K- nearest neighbor (KNN) is best for classification of images [13]. A Deep Neural Network (DNN) is an ANN with multiple layers hidden between the input and output planes. Artificial Neural Networks (ANN) are mathematical constructs originally developed for biological neurons. Each "neuron" is a relatively simple element, for example, by adding its inputs and applying a threshold to the result to determine the output of that "neuron". Several decades of research have focused on finding out how to build network architectures using these mathematical constructs and how to automatically define the weighting in each of the connections between neurons to perform a variety of tasks. For example, RNAs can do things like recognizing handwritten numbers. A "biological neuron network" would mean any group of connected biological nerve cells. Your brain is a biological neural network, so multiple neurons in a shell grow together to form synaptic connections. The term "biological neural network" is not very accurate; It does not define a specific biological structure. In the same way, an ANN may mean any of a large number of mathematical constructs similar to "neurons" connected in various ways to perform any one of a large number of tasks. The convolutional neural network (CNN or ConvNet) is a type of deep progress network that has been successfully applied to visual image analysis. CNN uses a multilayer perceptron variation that has been designed to require a minimum of pretreatment. They are also called spatially invariant or invariant neural networks (SIANN) based on their jointly weighted architectural and invariant properties. The Lenet model does not work on large images, while Alexnet works on large images. Cnn is used for the object Recognition and Recursive Neural Network (RNN) is used for speech recognition. In Recursive Neural Network Signal travelling in both directions by introducing loops in the network. The performance and potential of SVM was found higher than k-NN and Naive Thomas Bayes with recognition rate 93%. Chain code provides higher performance with SVM classifier [14]. 5. CONCLUSION Deep Convolutional Neural Network have shown superior results to traditional shallow networks in many recognition tasks. Using Deep CNN Increases test accuracy by nearly 1 percent. HMM classes for character recognition owing to the distinct advantage of adjustment of pen – thickness and interpolation. Standardization of the sequences of stroke order is a way to reduce the overhead of look-up table used for recognition of character in HMM class. For single digit recognition, Convolutional neural nets present impressive results. Conventional Neural network gives impressive performance, that performance is somewhat mysterious. Generating modified set of fragments is a lengthy process. Using polygonal shape, vectors are created which does not a give assurance of generation of exact image. Contours are the shape descriptor in object recognition problem. Power quality event gives the clarity that multiple images can use PQ event and generate waveforms for multiple images at same time. References
  7. 7. © IJCIRAS May 2018 | Vol. 1 Issue. 1 IJCIRAS1004 WWW.IJCIRAS.COM 20 [1] Ema Rachmawati, Masayu Leylia khodra, Iping Supriana,School of Electrical Engineering and Informatics Institut Teknologi Bandung,“Shape based recognition using Freeman chain code and Modified Needleman - Wunsch ” 978-1-5090- 4139-8/16/$31.00 ©2016 IEEE. [2] Hormigo, J. and Villalba, J. (2016). “New Formats for Computing with Real-Numbers under Round- to-Nearest.” IEEE Transactions on Computers, 65(7), pp.2158-2168. [3] Leandro Rodrigues Manso Silva, Paulo Fernando Ribeiro, Federal University of Juiz de Fora Juiz de Fora – MG, Brazil ,“Power Quality Waveform Recognition Using Google Image Search Engine (iPQ-Google)” 978-1-5090-3792-6/16/$31.00 ©2016 IEEE. [4] Shailesh Acharya , Ashok Kumar Pant , Prashnna Kumar Gyawali, Institute Of Engineering Tribhuvan University Kathmandu, Nepal,“Deep Learning Based Large Scale Handwritten Devanagari Character Recognition” 978-1-4673- 6744-8/15/$31.00 ©2015 IEEE [5] Ashish Tripathi, Sankha Subhra Paul, Vinay Kumar Pandey, CSED, MNNIT, Allahabad, India “Standardization of stroke order for online Isolated Devanagari Character Recognition for iPhone” [6] Raid Saabni School of Computer Science Tel-Aviv Yaffo Academic College, Tel-Aviv, Israel Triangle Research & Development Center.“Recognizing handwritten Single Digits and Digit Strings Using Deep Architecture of Neural Networks” 978-1- 4673-9187-0 ©2016 IEEE [7] Monica Patel, Shital P. Thakkar,“Handwritten Character Recognition in English: A Survey“ International Journal of Advanced Research in Computer and Communication Engineering Vol. 4, Issue 2, February 2015 [8] Hirali S. Amrutiya, Payal P. Moghariya and Vatsal H. Shah “handwritten character recognition“ International Journal of Advance Engineering and Research Development (IJAERD) Special Issue, Volume 1,Issue 4, April 2014, e-ISSN: 2348 – 4470. [9] Ibrahim, Ratnawati & Abu Hassan, Mohd Fadzil & Zainudin, Nurnashriq & Syahidi Aisha, Mohd. (2011). “Number Recognition System Using Chain Code Technique” December 2011. [10] Dhannoon, Ban N. & H. Al, Huda. (2013). “Handwritten Hindi Numerals Recognition.” International Journal of Innovation and Applied Studies. [11] Pulipati Annapurna, Sriraman Kothuri, and Srikanth Lukka, “Digit Recognition Using Freeman Chain Code” International Journal of Application or Innovation in Engineering & Management (IJAIEM), Volume 2, Issue 8, August 2013. [12] T Siva Ajay,“Handwritten Digit Recognition Using Convolutional Neural Networks” International Research Journal of Engineering and Technology (IRJET) Volume: 04 Issue: 07, July -2017. [13] Apoorva Chaudhary and Mr.Roshan lal chhoker ,“Handwritten Hindi Numeric Character Recognition and comparison of algorithms” 978- 1-5090-3519-9/17/$31.00©2017 IEEE [14] Fating, Kshama & Ghotkar, Archana. (2014).”Performance Analysis of Chain Code Descriptor for Hand Shape Classification.” International Journal of Computer Graphics & Animation. 4. 9-19. 10.5121/ijcga.2014.4202.

The field of Artificial Intelligence is very fashionable today, especially neural networks that work well in various areas such as speech recognition and natural language processing. This Research Article briefly describes how deep learning models work and what different techniques are used in text recognition. It also describes the great progress that has been made in the field of medicine, the analysis of forensic documents, the recognition of license plates, banking, health and the legal industry. The recognition of handwritten characters is one of the research areas in the field of artificial intelligence. The individual character recognition has a higher recognition accuracy than the complete word recognition. The new method for categorizing Freeman strings is presented using four connectivity events and eight connectivity events with a deep learning approach.

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