Ocr Essays

A2 OCR History F966 How to structure essays for this paper 1

For those of you who have studied Russia at AS clearly there is some areas of overlap -such as content, however obviously there are also some distinct differences. For instance this is a 60 mark question and last year it was only 50 marks. Additionally, this year you are looking at a 100 year time frame. Finally you need to approach these exam questions thematically and you would never have been asked to do that previously. I suggest joining the student room for all your subjects and topics, posting questions, joining threads and getting other students to give you feedback on your essays will be useful. http://www.thestudentroom.co.uk/ Additionally I also suggest that you tackle this unit thematically, because clearly it is thematic and thus requests you to show synthesis in your essay writing. Moreover it is also important to not adopt a chronological approach but rather to use a thematic approach and synthesis, thus include, evaluation rather than merely narrative and if possible avoid adopting a narrative approach, please also apply equally to both questions. The examiners are looking to see in your work, meaningful comparative arguments and better grades will be awarded if you use, direct comparison/thematic approaches. It is also important that you assess relevant issues of continuity and change within your essay questions, to assure allocation to the top band of the exam mark scheme. Your introductions and conclusions should be brief and be mere summaries of the main points discussed in your question or to be discussed and generally just revealing what your answer to the question is. Last year you would have been advised to tackle your essay questions keeping in mind political, social and economic issues. This year I would suggest in your study skills as well as your actual exams that each individual historical event should be broken down into its key themes, this could be done on cards, in mind maps in your historical timeline or any other way that works for you. As you already know in the Russia paper there are 4 themes and these are as follows: (1) society, (2) economy, (3) nature of government & (4) war, these can be used virtually generically for most questions that you will answer in the exams, this then should assure you essay is both thematic and synoptic. .

Themes Society Economy Nature of Government War

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Optical Character Recognition (OCR)

INTRODUCTION
A major goal of pattern recognition is to reduce human participation capabilities in artificial systems. As a special aspect of visual perception, the ability to read machine printed or hand written text is one such remarkable ability of human that is even today hardly matched by machine intelligence. Since the every first effort to achieve optical character recognition (OCR), i.e. to automatically read machine printed texts, the research field dealing with artificial reading systems has undergone significant changes in methodology and made considerable progress towards its ultimate goal.
Optical Character Recognition (OCR) is a process of automatic recognition of different characters from a document image. OCR systems are considered as a branch of artificial intelligence and a branch of computer vision as well. Researchers classify OCR problem into two domains. One deals with the image of the character by scanning which is called Off-line recognition. The other has different input way, where the writer writes directly to the system using, for example, light pen as a tool of input. This is called On-line recognition. Fig. 1 shows the block diagram of the typical OCR system. The online problem is usually easier than the offline problem since more information is available, like the movement of the pen may be used as a feature of the character [1]. These two domains (offline & online) can be further divided into two areas according to the character itself: the recognition of machine printed data and the recognition of handwritten data. Machine printed characters are uniform in size, position and pitch for any given font. In contrast, handwritten characters are non-uniform; they can be written in many different styles and sizes by different writers and at different times even by the same writer. The OCR system based on three main stages: pre-processing, feature extraction and discrimination (also called classifier or recognition engine).


Figure 1: Typical OCR block diagram [2]

Traditional OCR systems are suffering from two main problems, one comes from feature extraction stage and the other comes from classifier (recognition stage). Feature extraction stage is responsible for extracting features from the image and passing them as global or local information to the next stage in order to help the later taking decision and recognizing the character. Two challenges are faced: if feature extractor extracts many features in order to offer enough information for classifier, this means many computations as well as more complex algorithms are needed. Thus long processor time will be consumed. On the other hand, if few features are extracted in order to speed up the process, insufficient information may be passed to classifier. The second main problem that classifier is responsible for, is that most of classifiers are based on Artificial Neural Networks (ANNs). However, to improve the intelligence of these ANNs, huge iterations, complex computations and learning algorithms are needed, which also lead to consume the processor time. Therefore, if the recognition accuracy is improved, the consumed time will increase and vice versa.

To tackle these problems, a new OCR construction is not proposed in this paper, where features extractor nor is ANN needed. The proposed construction relies on the image compression technique (JPEG). Taking advantages of the compressor, that it compresses the image by encoding only the main details and quantizes or truncates the remaining details (redundancy) to zero. Then generates a unique vector (code) corresponding to the entire image. This vector can be effectively used to recognize the character since it carries the main details of the character's image. The importance of the main details is that they are common amongst the same character which is written by different writers.

Recognizing handwritten numerals is an important area of research because of its various application potentials. Automating bank cheque processing, postal mail sorting, and job application form sorting, automatic scoring of tests containing multiple choice questions and other applications where numeral recognition is necessary. Character recognition engine for any script is always a challenging problem mainly because of the enormous variability in handwriting styles. A recognition system must be therefore robust in performance so that it may cope with the large variations arising due to different writing habits of different individuals.

Figure 2: UNIVERSITY LETTER
1.1 Scope
The goal of this paper is to provide a comprehensive overview of the application of JPEG algorithms in the research field of offline handwritten numeral recognition. Techniques for automatic handwritten numeral recognition can be distinguished as being either online or offline, depending on the particular processing strategy applied. Online recognition is performed as the number is to be recognised is written. Therefore handwriting has to be captured online, i.e. using some pressure sensitive devices. They provide rich sequence of sensor data, which is the big advantage of online approaches. In offline recognition is performed after the text has been written. For this purpose, images of the handwriting are processed, which are captured using scanner or a camera. This paper emphasizes approaches addressing the challenging task of offline hand writing kannada numeral recognition. We concentrate on most widely used JPEG algorithms.
Although handwritten kannada numerals recognition shows parallels to classical OCR, i.e. the analysis of machine printed text, the scope of this paper is limited to handwritten kannada numeral recognition.
1.2 History (of JPEG algorithm)
The JPEG committee was formed in 1986 by the CCITT and ISO Standards organizations to set worldwide standards for image compression. This work was technically complete by early 1991 and latter approved as an International Standards Organization (ISO). Originally, JPEG targeted full-color still frame applications, achieving a 15:1 average compression ratio.
The JPEG baseline system decomposes the input image into (8x8) pixels source blocks. Then, every block is divided into smaller parts based on the differences in color, and a DCT transformation is applied to these parts. The DCT is performed on (8x8) pixel blocks to transfer the blocks into the frequency domain, and the coefficients are then quantizated and entropy coded for compression. Based on an (8x8) block, the theoretical limit for the maximum achievable compression ratio would be 64:1, but in reality, usable compression ratios are much less than that. Thus, in case of a block that consists of only one color, the value after the DCT transformation is a single value.
1.3 Structure
The reminder of this article is organised as follows. In the following section, we will first give

2. RELATED WORK
In this paper, Devanagri numeral recognition algorithm is proposed based on JPEG image compression algorithm. The aim of handwritten numeral recognition (HNR) system is to classify input numeral as one of K classes. Over the years, considerable amount of work has been carried out in the area of HNR. Various methods have been proposed in the literature for classification of handwritten numerals. These include Hough transformations, histogram methods, principal component analysis, and support vector machines, nearest neighbour techniques, neural computing and fuzzy based approaches [3]-[4]. A study on different pattern recognition methods are given in [5]-[6]. In comparison with HNR systems of various non Indian scripts [e.g. Roman, Arabic, and Chinese], we find that the recognition of handwritten numerals for Indian scripts is still a challenging task and there is spurt for work to be done in this area. Few works related to recognition of handwritten numerals of Indic scripts can be found in the literature [7]-[10]. A brief review of work done in recognition of handwritten numerals written in Devanagri script is given below:
Many schemes for digit classification have been reported in literature. They mostly differ in feature extraction schemes and classification strategies (Govindan & Shivaprasad 1990; Trier et al 1996) [11]. Features used for recognition tasks include topological features, mathematical moments etc. Classification schemes applied include nearest neighbour schemes and feed forward networks. In order to make their systems robust against variations in numeral shapes, researchers have also used deformable models, multiple algorithms and learning. A survey of the techniques is provided by Amin (1997) [12] and Plamondon & Srihari (2000) [13], Lam & Suen (1986) [14] have used a fast structural classifier and a relaxation-based scheme which uses deformation for matching.
A knowledge-based system using multiple experts has been used by Mai & Suen (1990) [15]. Kimura & Sridhar (1991) [16] developed a statistical classification technique that utilized profiles and histograms of the direction vectors derived from the contours. Chen & Lieh (1990) [17] proposed a two layer random graph based scheme which used components and strokes as primitives. Jain & Zongkar (1997) [18] have proposed a recognition scheme using deformable templates. LeCun et al (1989) [19] suggested a novel back propagation based neural network architecture for handwritten zip code recognition. Knerr et al (1992) [20] suggested the use of neural network classifiers with single layer training for recognition of handwritten numerals. Wang & Jean (1993) [21] suggested use of neural networks for resolving confusion between similar looking characters. Among studies on Indian scripts, notable work has been done on recognition of printed Devanagari characters by Sinha and others (Sinha & Mahabala 1979[22]; Bansal & Sinha 2001) [23]. They also suggested contextual post processing for Devanagri character recognition and text understanding. For handwritten Bengali character recognition, Dutta & Chaudhury (1993) [24] presented a curvature feature based approach. Chaudhuri & Pal (1998) [25] presented a complete Bangla OCR system.
3. DATA SET CHARACTERISTICS:
Devanagri script, originally developed to write Sanskrit, has descended from the Brahmi script sometime around the 11th century AD. It is adapted to write many Indic languages like Marathi, Mundari, Nepali, Konkani, Hindi and Sanskrit itself. Marathi is an Indo-Aryan language spoken by about 71 million people mainly in the Indian state of Maharashtra and neighbouring states. Since 1950 Marathi has been written with the Devanagri alphabet. Figure 2 below presents a listing of the symbols used in Marathi for the numbers from zero to nine.


Figure 2: Numerals 0 to 9 in Kannada script
The dataset of Marathi handwritten numerals 0 to 9 is created by collecting the handwritten documents from writers. Data collection is done on a sheet specially designed for data collection. Writers from different professions and age groups were chosen and were asked to write the numerals. A sample sheet of handwritten numerals is shown in figure 3.

Figure 3: Sample sheet of handwritten numerals
The collected data sheets were scanned using a flat bed scanner at a resolution of 300 dpi and stored as colour images. The raw input of the digitizer typically contains noise due to erratic hand movements and inaccuracies in digitization of the actual input. To bring uniformity among the numerals, the cropped numeral image is size normalized to fit into a size of 60x60 pixels. A total of 400 binary images representing Marathi handwritten numerals are obtained from 20 different subjects.
4. JPEG COMPRESSION TECHNIQUE:
JPEG may be adjusted to produce very small compressed images that are of relatively poor quality in appearance but still suitable for many applications. Conversely, JPEG is capable of producing very high quality compressed images that are still far smaller than the original uncompressed data. JPEG is also different in that it is primarily a lossy method of compression. Most popular image format compression schemes such as RLE, LZW or the CCITT standards are lossless compression methods. That is, they do not discard any data during the encoding process. An image compressed using a lossless method is guaranteed to be identical to the original image when uncompressed.

Figure 4(a): JPEG Encoder
Lossy schemes, on the other hand, throw useless data away during encoding. This is in fact, how lossy schemes manage to obtain superior compression ratios over most lossless schemes. JPEG was designed specifically to discard information that the human eye cannot easily see. Slight changes in color are not perceived well by the human eye while slight changes in intensity (light and dark) are. Therefore JPEG's lossy encoding tends to be more frugal with the gray scale part of an image and to be more frivolous with the color.


Figure 4(b): JPEG Decoder

In the JPEG baseline coding system, which is based on the discrete cosine transform (DCT) and is adequate for most compression applications, the input and output images are limited to 8 bits, while the quantized DCT coefficient values are restricted to 11 bits. The human vision system has some specific limitations which JPEG takes advantage of, to achieve high rates of compression.
As can be seen in the simplified block diagram of Figure 4, the compression itself is performed in four sequential steps: 8x8 sub-image extraction, DCT computation, quantization, and variable-length code assignment i.e. by using symbol encoder.
The JPEG compression scheme is divided into the following stages:
1. Transform the image into an optimal colour space.
2. Downsample chrominance components by averaging groups of pixels together.
3. Apply a Discrete Cosine Transform (DCT) to blocks of pixels, thus removing redundant image data.
4. Quantize each block of DCT coefficients using weighting functions optimized for the human eye.
5. Encode the resulting coefficients (image data) using a Huffman variable word-length algorithm to remove redundancies in the coefficients.
Since we do not concern in this work about the reconstruction part, the only part of compression is used (dashed box) and the vector will be tapped immediately after quantization stage.

5. PROPOSED ALGORITHM
Figure 5 illustrates the sequence of the proposed algorithm's steps based on reference [3]. After the character's image is scanned in the system the JPEG approximation will produce a vector. This vector is assumed to uniquely represent input image since it carries the important details of that image. Figure 6 shows a sample for Devnagri numeral 0. Then Euclidean distance between this vector and each vector in codebook will be measured. Finally, the minimum distance points to the corresponding character, and then the character is recognized. To obtain higher recognition accuracy additional data of length of the vector produced is also used in recognition process.
5.1 System Components:
The two main components are the code of the compression stage: 1. JPEG compressor and 2. The codebook as shown in Figure 6. JPEG compressor produces the vector which is assumed to uniquely represent input image since it carries the important details of that image. The code book is obtained by taking average of each group of Devnagri numeral. Code book design procedure is explained in following section.

 

Figure 5: Flowchart of the proposed algorithm

Figure 6: Graph of a sample JPEG approximation vector for Kannada number number 0

5.2 Codebook building
The codebook can be built as following:
1. Get 400 vectors for all available database (our database contains 400 written numerals).
2. Group the 400 vectors according to their represented numerals. For instance, the group of number 0 has (in our database) 40 different 0's that were written by 40 different writers so it will have 40 vectors.
3. Average each group which results in unique vector for each group. These are the codes located in the codebook.

5.3 Classifier:
After obtaining code book, last step is numeral recognition which is implemented using Euclidean distance classifier. The Euclidean distance classifier is used to examine accuracy of the system designed. The Euclidean distance (d) between two vectors X and Y can be defined as:

Expression

RESULT AND DISCUSSION
JPEG compression property yields high compression ratio which results in minimum image size. Every compressed image has a unique vector which helps to identify each numeral. By using this unique vector, the proposed system has recognized the input numeral after measuring the Euclidean distance between the vector and the vectors in the codebook, then the shortest distance pointed to the corresponding numeral. In addition to the advantage of speed using codebook, it can be universal by means of character's nature (language, writing mode) as well as character's image size. We used 60x60 8-pixel color image as input image.

Kannada Numeral Percentage of Accuracy
Table 1: The recognition accuracy

The code book is obtained with the help set of available database. The proposed algorithm is tested on input numerals and the accuracy of percentage recognitions for each character obtained. The individual and average recognition accuracy of numeral is shown table 1. The system was able to recognize the characters during short time comparing with any existing system using ANN because it saves time taken by features extractor as well as it uses codebook. The code book is obtained with the help set of available database. The proposed algorithm is tested on input numerals and the accuracy of percentage recognitions for each character obtained. The individual and average recognition accuracy of numeral is shown table 1. The system was able to recognize the characters during short time comparing with any existing system using ANN because it saves time taken by features extractor as well as it uses codebook


Figure 7: Confusing handwritten numerals
7. CONCLUSION
A fast and robust method is proposed in this paper for achieving better recognition rates for
handwritten Devnagri numerals which is not based on ANN to avoid the time consuming
problems. It is based JPEG image compression algorithm which generates unique vector which
helps to identify each numeral. The result was considerably high in terms of recognition
rate.Our future work aims to improve classifier to achieve still better recognition The proposed
method can be extended to recognition of numerals of other Indic scripts.

REFERENCES
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3. Abdurazzag Ali Aburas and Salem Ali Rehiel, 'JPEG for Arabic Handwritten Character Recognition: Add a Dimension of Application', Advances in Robotics, Automation and Control ISBN 78-953-7619-16-9, pp. 472, October 2008.
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[12] Amin A. (1997), 'Off-line Arabic character recognition: Survey', Proc. 4th Int. Conf. on Document
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AUTHORS
Gajanan Birajdar is working as Assistant Professor in the department of Electronics & Telecommunication Engineering at SIES Graduate School of Technology, Navi Mumbai, India. He obtained B.E. (Electronics) from Dr. BAM University, Aurangabad, Maharashtra and M. Tech. (Elect. & Telecom) from Dr. BAM Technological University, Lonere, India. He has been in teaching for the past 14 years. He is life member of ISTE and IETE. He has attended several seminars and workshops. He has published papers in international journals. His area of research includes Ad hoc networks, image and speech processing.
Mansi Subhedar is working as Lecturer in the department of Electronics & Telecommunication Engineering at SIES Graduate School of Technology, Navi Mumbai, India. She obtained B.E. (Elect. & Telecom) from Dr. BAM University, Aurangabad, Maharashtra and M.E. (Electronics) from Mumbai University. She has been in teaching for the past five years. She is life member of ISTE. She has attended several workshops and conferences. She has published and presented papers in various national conferences across India. Her research area includes next generation networks, sensor networks and signal processing.

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