OPTICAL CHARACTER RECOGNITION Ku. Priti V. Manjare 1, Vaishali B. Farkade 2, Mr. Jha 3

Transcription

1 OPTICAL CHARACTER RECOGNITION Ku. Priti V. Manjare 1, Vaishali B. Farkade 2, Mr. Jha 3 Priti V. Manjare, Information Technology, S.R.P.C.E,Nagpur,manjarepreeti25@gmail.com Vaishali B. Farkade, Information Technology, S.R.P.C.E, Nagpur,vaishalifarkade143@gmail.com Mr. Jha Sir, Information Technology, S.R.P.C.E, Nagpur,,dablu_jha135@yahoo.co.in ABSTRACT: At the present time, keyboarding remains the most common way of inputting data into computers. This is probably the most time consuming and labour intensive operation. OCR is the machine replication of human reading and has been the subject of intensive research for more than three decades. OCR can be described as Mechanical or electronic conversion of scanned images where images can be handwritten, typewritten or printed text. It is a method of digitizing printed texts so that they can be electronically searched and used in machine processes. It converts the images into machine-encoded text that can be used in machine translation, text-to-speech and text mining. This paper presents a simple, efficient, and less costly approach to construct OCR for reading any document that has fix font size and style or handwritten style. To achieve efficiency and less computational cost, OCR in this paper uses database to recognize English characters which makes this OCR very simple to manage. The aim of the project OCR is to develop. OCR software for online/offline handwriting recognition. OCR is an optical character recognition and is the mechanical or electronic translation of images of handwriteen or type written text(usally captured by a scanner) into machine-editable text.ocr is a field of research in pattern recognition, artificial intelligence and machine vision. Handwritten recognition is used most often to describe the ability of a computer to translate human writing into text. This may take in one of the two ways, either by scanning of written text or by writing directory on peripheral input devices. In this project, we present an OCR for printed English image in text script. Text written in English script,there is no separation between the characters. English is one of the most spoken language in World. About 300 million people speak English. One of the important reasons for poor reconition rate in optical character recognition rate in optical character recognition (OCR) system is the error in character segmentation. KEYWORDS: Scanned images, digitizing, translation, machine encoded text, fix font, handwritten style, text-to-speech. INTRODUCTION: The advancements in pattern recognition has accelerated recently due to the many emerging applications which are not only challenging, but also computationally more demanding, such evident in Optical Character Recognition (OCR), Document Classification, Computer Vision, Data Mining, Shape Recognition, and Biometric Authentication, for instance.[13] The area of OCR is becoming an integral part of document scanners, and is used in many applications such as postal processing, script recognition, banking, security (i.e. passport authentication) and language identification. The research in this area has been ongoing for over half a century and the outcomes have been astounding with successful recognition rates for printed characters exceeding 99%, with significant improvements in performance for handwritten

2 cursive character recognition where recognition rates have exceeded the 90% mark. [13] Nowadays, many organizations are depending on OCR systems to eliminate the human interactions for better performance and efficiency. Optical Character Recognition also referred to as OCR is a system that provides a full alphanumeric recognition of printed or handwritten characters at electronic speed by simply scanning the document [2]. Documents are scanned using a scanner and are given to the OCR systems which recognizes the characters in the scanned documents and converts them into ASCII data. [2] In OCR a database is used at the back end for recognition. In proposed system the process consists of following processing steps: (1) Scanning of Image, (2) PreProcessing of Image (3) Character Extraction (4) Feature Extraction and Recognition (5) Post-Processing. [1] In the document scanning step, a scanner is used to scan the handwritten or printed documents. The quality of the scanned document depends up on the scanner. So, a scanner with high speed and color quality is desirable. The recognizing process includes several complex algorithms and previously loaded templates and dictionary which are crosschecked with the characters in the document and the corresponding machine editable ASCII characters. The verifying is done either randomly or chronologically by human Intervention [2]. Optical Character Recognition is classified into two types, Offline recognition and Online recognition. In offline recognition the source is either an image or a scanned form of the document whereas in Online recognition the successive points are represented as a function of time and the order of strokes are also available RELATED WORK : Claudiu et al. (2011) [1] has investigated using simple training data pre-processing gave us experts with errors less correlated than those of different nets trained on the same or bootstrapped data. Hence committees that simply average the expert outputs considerably improve recognition rates. Our committee-based classifiers of isolated handwritten characters are the first on par with human performance and can be used as basic building blocks of any OCR system (all our results were achieved by software running on powerful yet cheap gaming cards). Georgios et al. (2010) [2] has presented a methodology for off-line handwritten character recognition. The proposed methodology relies on a new feature extraction technique based on recursive subdivisions of the character image so that the resulting sub-images at each iteration have balanced (approximately equal) numbers of foreground pixels, as far as this is possible. Feature extraction is followed by a two-stage classification scheme based on the level of granularity of the feature extraction method. Classes with high values in the confusion matrix are merged at a certain level and for each group of merged classes, granularity features from the level that best distinguishes them are employed. Two handwritten character databases (CEDAR and CIL) as well as two handwritten digit databases (MNIST and CEDAR) were used in order to demonstrate the effectiveness of the proposed technique. Sankaran et al. (2012) [3] has presented present a novel recognition approach that results in a 15% decrease in word error rate on heavily degraded Indian language document images. OCRs have considerably good performance on good quality documents, but fail easily in presence of degradations. Also, classical OCR approaches perform poorly over complex scripts such as those for Indian languages. Sankaran et al. (2012) [3] addressed these issues by proposing to recognize character n-gram images, which are basically groupings of consecutive character/component segments. Their approach was unique, since they use the character n- grams as a primitive for recognition rather than for post- processing. By exploiting the additional context present in the character n-gram images, we enable better disambiguation S between confusing characters in the recognition

3 phase. The labels obtained from recognizing the constituent n-grams are then fused to obtain a label for the word that emitted them. Their method is inherently robust to degradations such as cuts and merges which are common in digital libraries of scanned documents. We also present a reliable and scalable scheme for recognizing character n-gram images. Tests on English and Malayalam document images show considerable improvement in recognition in the case of heavily degraded documents. Jawahar et al. (2012) [4] has propose a recognition scheme for the Indian script of Devanagari. Recognition accuracy of Devanagari script is not yet comparable to its Roman counterparts. This is mainly due to the complexity of the script, writing style etc. Our solution uses a Recurrent Neural Network known as Bidirectional Long- Short Term Memory (BLSTM). Our approach does not require word to character segmentation, which is one of the most common reason for high word error rate. Jawahar et al. (2012) [4] has reported a reduction of more than 20% in word error rate and over 9% reduction in character error rate while comparing with the best available OCR system. Zhang et al. (2012) [5] has discussed the misty, foggy, or hazy weather conditions lead to image color distortion and reduce the resolution and the contrast of the observed object in outdoor scene acquisition. In order to detect and remove haze, this article proposes a novel effective algorithm for visibility enhancement from a single gray or color image. Since it can be considered that the haze mainly concentrates in one component of the multilayer image, the haze-free image is reconstructed through haze layer estimation based on the image filtering approach using both low-rank technique and the overlap averaging scheme. By using parallel analysis with Monte Carlo simulation from the coarse atmospheric veil by the median filter, the refined smooth haze layer is acquired with both less texture and retaining depth changes. With the dark channel prior, the normalized transmission coefficient is calculated to restore fogless image. Experimental results show that the proposed algorithm is a simpler and efficient method for clarity improvement and contrast enhancement from a single foggy image. Moreover, it can be comparable with the state-of-the-art methods, and even has better results than them. Badawy, W. et al. (2012) [6] has discussed the Automatic license plate recognition (ALPR) is the extraction of vehicle license plate information from an image or a sequence of images. The extracted information can be used with or without a database in many applications, such as electronic payment systems (toll payment, parking fee payment), and freeway and arterial monitoring systems for traffic surveillance. The ALPR uses either a color, black and white, or infrared camera to take images. Ntirogiannis et al. (2013) [7] has studied that the document image binarization is of great importance in the document image analysis and recognition pipeline since it affects further stages of the recognition process. The evaluation of a binarization method aids in studying its algorithmic behavior, as well as verifying its effectiveness, by providing qualitative and quantitative indication of its performance. This paper addresses a pixel-based binarization evaluation methodology for historical handwritten/machine-printed document images. In the proposed evaluation scheme, the recall and precision evaluation measures are properly modified using a weighting scheme that diminishes any potential evaluation bias. Yang et al. (2012) [8] has proposed a novel adaptive binarization method based on wavelet filter is proposed in this paper, which shows comparable performance to other similar methods and processes faster, so that it is more suitable for real-time processing and applicable for mobile devices. The proposed method is evaluated on complex scene images of ICDAR 2005 Robust Reading Competition, and experimental results provide a support for our work.. APPLICATION:

4 OCR engine has been developed into many kinds of object oriented OCR applications, such as receipt OCR, invoice OCR, check OCR, legal billing document OCR. It can be used for: Data entry for business documents, e.g. check, passport,invoice,bank statement and receipt Automatic number plate recognition Automatic insurance documents keys information extraction Extracting business card information into a contact list Make electronic images of printed documents searchable, e.g. Google Books Converting handwriting in real time to control a computer (pen computing) Assistive technology for blind and visually impired users. PROPOSED SYSTEM: The architecture of the optical recognition system on a grid infrastructure consists of the the three main components.they are- - Scanner - OCR hardware or software Our proposed OCR system is based on grid infrastructure, which is a character recognition system that supports recognition of the characters of multiple languages. This feature is what we call grid infrastructure which eliminates the problem of heterogeneous character recognition and supports multiple functionalities to be performed on the document. The multiple functionalities include editing and searching too where as the existing system supports only editing of the document. In this context, Grid infrastructure means the infrastructure that supports group of specific set of languages. Thus OCR on a grid infrastructure is multi-lingual. The main purpose of Optical Character Recognition (OCR) system based on a grid infrastructure is to perform Document Image Analysis, document processing of electronic document formats converted from paper formats more effectively and efficiently. This improves the accuracy of recognizing the characters during document processing compared to various existing available character recognition methods. Here OCR technique derives the meaning of the characters, their font properties from their bit-mapped images. The primary objective is to speed up the process of character recognition in document processing. As a result the system can process huge number of documents with-in less time and hence saves the time.since our character recognition is based on a grid infrastructure, it aims to recognize multiple heterogeneous characters that belong to different universal languages with different font properties and alignments. Module Description: Our software system Optical Character Recognition on a grid infrastructure can be divided into five modules based on its functionality.the modules classified are as follows:- Document Processing Module System Training Module. Document Recognition Module. Document Editing Module and Document Searching Module. Document Processing Module

5 Scanning printed documents. Storing the documents as images. Processing those image-based documents. Converting these image-based documents into e-documents(also called structured documents) Recognizing the characters in documents. Generating grid infrastructure datastructure. System Training Module Training the system with the pre-defined fonts. Training the system with the new fonts that are not present in the system and that cannot be identified by the system. Document Recognition Module Converts the document into specific format Recognizes the characters Heterogeneous character Recognition Document Editing Module Addition of specific content to the documents Deletion of certain content from documents Any other modification of documents. Document Searching Module This module can be accessed by both the administrator and the end-user during the search of the user required document to implement the character recogniiton process on it. The user requests the system to search for a particular document. Then the system finds the documents based on OCR methodology and returns the result of the search to the user.

6 ALGORITHM The input pattern is presented to the input layer of the network. These inputs are propagated through the network until they reach the output units. This forward pass produces the actual or predicted output pattern, because, back propagation is a supervised learning algorithm, the desired outputs are given as part of the training vector. The actual network outputs are subtracted from the desired outputs and an error signal is produced. This error signal is the basis for the back propagation step, whereby the errors are passed back through the neural network by computing the contribution of each hidden processing unit and deriving the corresponding adjustments needed to produce the correct output. The connection weights are then adjusted and the neural network has just learned from an experience. Once the network is trained, it will provide the desired output for any of the input patterns. The network undergoes supervised training, with a finite number of pattern pairs consisting of an input pattern and a desired or target output pattern. An input pattern is presented at the input layer. The neurons here pass the pattern activations to the next layer neurons, which are in a hidden layer. The outputs of the hidden layer neurons are obtained by the weights and the inputs, these hidden layer outputs become inputs to the output neurons, which process the inputs using an optional bias and a threshold function. The final output of the network is determined by the activations from the output layer. A similar computation, still based on the error in the output, is made for the connection weights between the input and hidden layers. The procedure is repeated with each pattern pair assigned for training the network. Each pass through all the training patterns is called a cycle or an epoch. The process is then repeated as many cycles as needed until the error is within a prescribed tolerance. The adjustment for the threshold value of a neuron in the output layer is obtained by multiplying the calculated error in the output at the output neuron and the learning rate and the momentum parameter used in the adjustment calculation for weights at this layer. TECHNOLOGY TO BE USED: we have using the following technology platforms: -GNU octave To develop and test the OCR software.

7 -5MP HD camera To take images for detenction -Output Interface FLOWCHART: FUTURE SCOPE: Today s uses of OCR are still somewhat limited to the scanning of the written word into useable In the future, the uses of OCR have been speculated to be far more advanced. Some of these advancements are already in the workings. Most believe that the use for reading the written text will diminish as Electronic Data Interchange (EDI) is used more and more, while paper document are phased out One of the current hopes for OCR is the chance of developing OCR software that can read compressed files. Text that is compressed into an image saved as ASCII or Hexadecimal data could be read by OCR and transferred back into readable text. CONCLUSION: Development of a system for recognizing Telugu fonts and characters based on Rough sets theory is presented. A survey of general OCR methodology in recent research was also summarized at the beginning of this report. 1. A new approach for simultaneously recognizing Telugu characters and font faces is developed. Identification of fonts is generally a neglected task in the existing OCRs.

8 Recognition of fonts and characters simultaneously is, hence, a major contribution of this thesis. 2. Explored the usefulness of rough sets in feature dimensionality reduction in the context of Telugu OCR. A great reduction that is also semantics-preserving is obtained in the feature set dimensions using RSAR. REFERENCES: [1] Junaid Tariq, Umar Nauman Muhammad Umair Naru., A-Soft: An English Language OCR Second International Conference on Computer Engineering and Application 2010 [2] A Review on the Various Techniques used for Optical Character Recognition, Pranob K Charles, V.Harish, M.Swathi, CH. Deepthi/ International Journal of Engineering Research and Applications (IJERA) ISSN: , Vol. 2, Issue 1,Jan-Feb [3] Character Recognition in practice Today and Tomorrow, 1996, Udo Miletzki, Siemens Electrocom GmbH D Konstanz, Germany. [4] Prototype Extraction and Adaptive OCR IEEE Transaction on pattern analysis and Machine Intelligence, VOL. 21, NO. 12, DECEMBER 1999, Yihong XU, Member, IEEE, George Nagy, Senior Member, IEEE. [5] Contextual Focus for Improved Recognition of Hand-Filled Forms, Wing Seong Wong, Nasser Sherkat, Tony Allen IRIS, Department of Computing. [6] Image processing Algorithms for Improved Character Recognition and Components Inspection, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC 2009), Anima Majumder. [7] A System for Automated Data Entry from Forms, 1996 IEEE Proceedings of ICPR 96, Raymond A. Lorie, V. P. Riyaz, Thomas K. Truong. [8] Combination of Document Image Binarization Techniques, 2011 International Conference on Document Analysis and Recognition. [9] ICAR: Identity Card Automatic Reader, IEEE, Josep Lladbs, Felipe Lumbreras, Vicente Chapaprieta, Joan Queralt. [10] Implementing Optical Character Recognition on the Android Operating System for Business Cards, IEEE 2010, Sonia Bhaskar, Nicholas Lavassar, Scott Green EE 368 Digital Image Processing. [11] Document Analysis and Recognition, Eighth International Conference on 29 Aug.-1 Sept. 2005, Alon, Jonathan. [12] en.wikipedia.org/wiki/optical_character_recognition [13] Pre-processing Techniques in Character Recognition, Yaseer Alginahi.