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Dissertations / Theses on the topic 'Handwritten numeral recognition'
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1
Legault, Raymond. "Unconstrained handwritten numeral recognition : a contribution towards matching human performance." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape11/PQDD_0004/NQ39794.pdf.
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Aktaruzzaman, M. "FEATURE EXTRACTION AND CLASSIFICATION THROUGH ENTROPY MEASURES." Doctoral thesis, Università degli Studi di Milano, 2015. http://hdl.handle.net/2434/277947.
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Entropy is a universal concept that represents the uncertainty of a series of random
events. The notion “entropy" is differently understood in different disciplines. In physics,
it represents the thermodynamical state variable; in statistics it measures the degree of
disorder. On the other hand, in computer science, it is used as a powerful tool for measuring
the regularity (or complexity) in signals or time series. In this work, we have
studied entropy based features in the context of signal processing.
The purpose of feature extraction is to select the relevant features from an entity. The
type of features depends on the signal characteristics and classification purpose. Many
real world signals are nonlinear and nonstationary and they contain information that
cannot be described by time and frequency domain parameters, instead they might be
described well by entropy.
However, in practice, estimation of entropy suffers from some limitations and is
highly dependent on series length. To reduce this dependence, we have proposed parametric
estimation of various entropy indices and have derived analytical expressions
(when possible) as well. Then we have studied the feasibility of parametric estimations
of entropy measures on both synthetic and real signals. The entropy based features
have been finally employed for classification problems related to clinical applications,
activity recognition, and handwritten character recognition. Thus, from a methodological
point of view our study deals with feature extraction, machine learning, and classification
methods.
The different versions of entropy measures are found in the literature for signals analysis.
Among them, approximate entropy (ApEn), sample entropy (SampEn) followed
by corrected conditional entropy (CcEn) are mostly used for physiological signals analysis.
Recently, entropy features are used also for image segmentation. A related measure
of entropy is Lempel-Ziv complexity (LZC), which measures the complexity of a
time-series, signal, or sequences. The estimation of LZC also relies on the series length.
In particular, in this study, analytical expressions have been derived for ApEn, SampEn,
and CcEn of an auto-regressive (AR) models. It should be mentioned that AR
models have been employed for maximum entropy spectral estimation since many
years. The feasibility of parametric estimates of these entropy measures have been
studied on both synthetic series and real data. In feasibility study, the agreement between
numeral estimates of entropy and estimates obtained through a certain number
of realizations of the AR model using Montecarlo simulations has been observed. This
agreement or disagreement provides information about nonlinearity, nonstationarity,
or nonGaussinaity presents in the series. In some classification problems, the probability
of agreement or disagreement have been proved as one of the most relevant
features.
VII
After feasibility study of the parametric entropy estimates, the entropy and related
measures have been applied in heart rate and arterial blood pressure variability analysis.
The use of entropy and related features have been proved more relevant in developing
sleep classification, handwritten character recognition, and physical activity
recognition systems.
The novel methods for feature extraction researched in this thesis give a good classification
or recognition accuracy, in many cases superior to the features reported in the
literature of concerned application domains, even with less computational costs.
3
Zhou, Jie. "Recognition and verification of unconstructed handwritten numerals." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0018/NQ47716.pdf.
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Oliveira, Luiz Eduardo Soares. "Automatic recognition of handwritten numerical strings." Mémoire, Montréal : École de technologie supérieure, 2003. http://wwwlib.umi.com/cr/etsmtl/fullcit?pNQ85289.
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Thèse (Ph.D.)--École de technologie supérieure, Montréal, 2003."Thesis presented to the École de technologie supérieure in partial fulfillment of the thesis requirement for the degree of philosophiae doctor in engineering". La numérotation de cet ouvrage est erronée. Bibliogr.: f. [149]-163. Également disponible en version électronique.
5
Dey, Susan A. (Susan Annette) 1976. "Adding feedback to improve segmentation and recognition of handwritten numerals." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/80059.
Full textAbstract:
Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.Includes bibliographical references (leaves 68-69).
by Susan A. Dey.
S.B.and M.Eng.
6
Chaudhry, Fawaz A. (Fawaz Altaf) 1978. "A system for offline automated recognition of unconstrained handwritten numerals." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/86800.
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Thesis (M.Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.Includes bibliographical references (leaves 68-69).
by Fawaz A. Chaudhry.
M.Eng.and S.B.
7
Chen, Yueting. "Handwritten numeral recognition using multiwavelets." Thesis, 2002. http://spectrum.library.concordia.ca/1812/1/MQ72929.pdf.
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In this report, we review different techniques for handwritten numeral recognition. More importantly we develop and test a hand-written numeral recognition system using multiwavelets. Given a black-and-white numeral, we first trace the contour of the numeral. Secondly we normalize and resample the contour points. Thirdly we perform multiwavelet orthonormal shell expansion on the contour points and we get several resolution levels and the average. We use the multiwavelet coefficients as the features to recognize the hand-written numerals. We use the L1 distance as a measure and the nearest neighbour rule as classifier for the recognition. The experimental result shows that it is a feasible way to use multi-wavelet features in handwritten numeral recognition.
8
李健宏. "Off-line Handwritten Numeral Recognition." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/53649071798915257751.
Full textAbstract:
碩士國立臺灣師範大學
工業教育學系在職進修碩士班
91
Recognition of off-line handwritten numerals has been the subject of research for many years. Since handwritten numerals widely vary in their shapes, recognizing them has been difficult and challenging. Although a high level of recognition has been achieved, the shortcomings of time-consuming learning and recognition still persist. The present research focuses on overcoming these defects, while maintaining a high recognition level. The research discussed in the present paper makes use of the MNIST database for learning and testing. For feature extraction, statistic features are used in the present research. Employing statistic features is saddled with the difficulty of a high number of dimensions, yet the present research, by using 130 dimensions, is able to distinguish between ten classifications. To make character recognition more effective, in the present research transformation by Fisher''s LDF (linear discriminant function) is applied to input characters. As experiments have shown, after transformation of non-clustered features (without learning) a level of recognition of 92.6% is achieved. In the present research, the method of WGLVQ, which is based on GLVQ (generalized learning vector quantization), is employed. Better convergence is achieved by GLVQ, and it is able to improve for LVQ. Experiments conducted within the current research have shown that both LVQ and GLVQ, applied to recognizing handwritten numerals, have quite good convergence behavior, also confirming the effectiveness of feature processing presented here. In the present research, the methods of LVQ and GLVQ are enhanced by weighting, yielding novel methods of WLVQ and WGLVQ. Therein, in every learning step, not only directions classifying reference vectors are adjusted, but also weights of every vector. With every step the weights of less-weighted vectors decrease, resulting in more pronounced distinctions of light and heavy weights. According to experiments, both WLVQ and WGLVQ exhibit more effective character recognition. With classification by WGLVQ and including learning, in an open test a level of recognition of 97.6% is achieved. With 16 clusters for each class, the recognition level rises to 98.2%. This result trails the level of 99.3% attained by Ernst using classification by LIRA, but while recognizing 10000 samples takes 30 minutes for the LIRA’s classification, the present approach allows recognition of 10000 samples in 1 - 2 minutes. The present research offers a more practical approach.
9
Wang, Kuo-Chuan, and 王國全. "Implementation of Handwritten Numeral Recognition in Space." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/10028335107564825731.
Full textAbstract:
碩士國立臺北教育大學
資訊科學系碩士班
101
This paper achieves the implementation of hand gesture digit recognition function by using the body motion sensor. The interactive motion-sensing technology is used to record the handwritten numerals in space. Then it converts the trajectories of handwritten numerals into corresponding shapes of digits. A feature matching method is also used to conduct the digit recognition. In the recognition process, the character area is used for extraction firstly. Then the image processing and recognition, binarization and Hilditch thinning algorithm are performed on the area of digits. The traversal and segmentation algorithm proposed by G.E.M.D.C. Bandara and S.D. Pathirana are also used to conduct segmentation of the skeleton area after thinning. The recognition’s result obtains by comparing the feature extraction and the feature database based on each character’s resulted segments. This paper proposes a concept of the interactive motion-sensing technology with trajectories of handwritten numerals to conduct digit recognition. And the conditions of identification of starter points can be improved. Therefore, this research can enhance the digit recognition rate and reduce the recognition time.
10
Sadri, Javad. "Automatic segmentation and recognition of unconstrained handwritten numeral strings." Thesis, 2007. http://spectrum.library.concordia.ca/975345/1/NR31139.pdf.
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Segmentation and recognition of handwritten numeral strings is a very interesting and challenging problem in pattern recognition. It also has a lot of important applications such as: postal code recognition, bank check processing; tax form reading, etc. In this thesis, a new system for the segmentation and recognition of unconstrained handwritten numeral strings is proposed. The system uses a combination of foreground and background features for the segmentation of touching numerals in strings. The method introduces new algorithms for the traversal of top and bottom foreground and background skeletons, and top and bottom contours of numerals. Then; it tries to locate all feature points on these skeletons and contours and alternatively match feature points from top to bottom (or bottom to top) of the images to build all possible candidate segmentation paths (so-called segmentation hypotheses). A novel genetic representation scheme is utilized in order to represent the space of all possible segmentation hypotheses. In order to improve searching and evolution of segmentation hypotheses and facilitate finding the ones with the highest confidence values of segmentation and recognition, this genetic framework utilizes contextual knowledge extracted from string images. A novel evaluation scheme based on segmentation and recognition scores is introduced in order to improve the evaluation of segmentation hypotheses and to enhance the outlier resistance of the system. In order to improve stability and plasticity of our system in the learning and recognition of numerals, a new algorithm for clustering of handwritten digits based on their shapes is proposed. Also, in order to improve the searching power of our system and its convergence, a new evolutionary algorithm based on genetic particle swarm optimization (GBPSO) is proposed. Numerous experiments using images from well known databases of handwritten numeral strings such as CENPARMI, NIST NSTRING SD19, and our newly created databases of Farsi/Arabic numerals have been conducted in order to evaluate the performance of the proposed method. Experiments have shown that proper use of contextual knowledge in segmentation; evaluation and search greatly improves the overall performance of the system. This system shows superior results compared with those reported in the literature.
11
Alamri, Huda. "Recognition of off-line arabic handwritten dates and numeral strings." Thesis, 2009. http://spectrum.library.concordia.ca/976645/1/MR63149.pdf.
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In this thesis, we present an automatic recognition system for CENPARMI off-line Arabic handwritten dates collected from Arabic Nationalities. This system consists of modules that segment and recognize an Arabic handwritten date image. First, in the segmentation module, the system explicitly segments a date image into a sequence of basic constituents or segments. As a part of this module, a special sub-module was developed to over-segment any constituent that is a candidate for a touching pair. The proposed touching pair segmentation submodule has been tested on three different datasets of handwritten numeral touching pairs: The CENPARMI Arabic [6], Urdu, and Dari [24] datasets. The final recognition rates of 92.22%, 90.43%, and 86.10% were achieved for Arabic, Urdu and Dari, respectively. Afterwards, the segments are preprocessed and sent to the classification module. In this stage, feature vectors are extracted and then recognized by an isolated numeral classifier. This recognition system has been tested in five different isolated numeral databases: The CENPARMI Arabic [6], Urdu, Dari [24], Farsi, and Pashto databases with overall recognition rates of 97.29% 97.75%, 97.75%, 97.95% and 98.36%, respectively. Finally, a date post processing module is developed to improve the recognition results. This post processing module is used in two different stages. First, in the date stage, to verify that the segmentation/recognition output represents a valid date image and it chooses the best date format to be assigned to this image. Second, in the sub-field stage, to evaluate the values for the date three parts: day, month and year. Experiments on two different databases of Arabic handwritten dates: CENPARMI Arabic database [6] and the CENPARMI Arabic Bank Cheques database [7], show encouraging results with overall recognition rates of 85.05% and 66.49, respectively.
12
Pan, Wei-Chih, and 潘維治. "Application of the Region Growing Algorithm on Handwritten Numeral Recognition." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/83769015283833014301.
Full textAbstract:
碩士大同大學
資訊工程學系(所)
92
In the handwritten numeral recognition system, researchers use a lot of features to help the recognition process, such as end points, fork points, strokes, circles, etc. These features were extracted from the original image as the basis for the numeral recognition. In this thesis, we propose a handwritten numeral recognition algorithm that based on the Region Growing algorithm, which is often applied in the image retrieval researches. With proper modification, this algorithm can also be applied to the handwritten numeral recognition. The Region Growing algorithm can simplify the image information based on the attribute of pixels of the original. Adjacent pixels with similar gray scale can be combined into the same region. After this operation, there will be spatially separable regions. Then based on the spatial distribution of these regions, we can find the similarity between images. Finally, we can classify and recognize the image according to the region’s distribution and similarity. Furthermore, we proposed a modified “Drop Falling algorithm” to deal with the segmentation problem of the original image. This algorithm in conjunction with the histogram can make proper cut trace and get accurate recognition results The experimental results show our proposed algorithm can achieve the accuracy requirement of handwritten numeric recognition system.
13
Kambar, Sapargali. "Generating synthetic data by morphing transformation for handwritten numeral recognition (with v-SVM)." Thesis, 2005. http://spectrum.library.concordia.ca/8504/1/MR10288.pdf.
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The amount of training data is one of the critical factors affecting the performance of handwritten numeral recognition system. One way to increase the training data size is adding synthesized data. In this study, synthetic data generation using morphing transformation with convex evolution is investigated. This technique uses a pair of original samples as the source and the target, and generates the synthetic samples by evolving the source towards the target. We aim to balance the data distribution. Normally, the training data has poor distribution due to the data redundancy and sparseness caused by frequent and rare samples, respectively. In terms of data clusters, some clusters are small, some are large and filling the gap between these clusters with synthetic data should smooth the clusters. Using the Support Vector Machines method, the rare samples, also called support vectors, are determined. Then, the number of rare samples is increased using morphing transformation. Using this technique a recognition rate of 99.19% has been achieved, while the initial performance without morphing was 99.07%. Morphing transformation generated more representative synthetic samples, which cannot be obtained by the other data synthesis methods such as affine and elastic distortions.
14
Lee, Shen-Wei, and 李聖偉. "A Study of Effective Region-features Extraction for Off-line Handwritten Numeral Recognition." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/nh7cy6.
Full textAbstract:
碩士國立臺中科技大學
資訊工程系碩士班
100
A multiple features extraction technique for the recognition of handwritten numbers is proposed. The proposed technique mainly extracts direction information from the structure of contours of each handwritten number and the direction information is integrated with a technique for detecting transitions among pixels and counting the number of cross lines in the lined image of offline handwritten numbers. The technique used in the recognition combining with a Support Vector Machine (SVM) classifier provides recognition rates up to 98.99%. This proposed technique also uses SVM for determining the effective features extracted from the multiple features of the handwritten number recognition.
15
Chen, Li-Lin, and 陳立麟. "A Bidirectional Recurrent Neural Network for Offline Connected and Overlapped Handwritten Numeral Recognition." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/8rwt48.
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碩士國立東華大學
資訊工程學系
106
In recent years, the duty technology tended to regarding the hand-written numeral identification maturely, regarding divides the successful independent numeral (Isolated Digit) all to be able to have the very high identification rate. But the digital identification still had the very big challenging in very many situations, was likely a digital non-pair of independent numeral which we wanted in the duty to recognize, but was the Unknown Length the numstring, this let us in divide (Segment) to obtain in the correct digital integer to be more difficult; Also has in the unknown length numstring to have links (Connect) even to have overlaps the (Overlapping) part, these factors will mistake which creates the division or recognizes, causes the whole identification rate drop. Regarding the above challenge, our paper first step use histogram of vertical projection obviously separates first cuts many digital fragment, then uses the Bidirectional Recurrent Neural Network by Sequence Labeling the way, will obtain all fragments will make the synchronized division and the identification movement. Moreover, our paper makes the training to the unknown length goal, and obtains for the solution training sample not easily. we provides the data augmentation method to synthesize the independent numeral to become the multi-integer string the new sample; Other also like Receptive Field, lets the neural network learn much better, as well as designs a confirmation method to come the result which obtains our nerve network, identifies the final digital result. The final identification rate in NSTRING SD19 database is 97.6%, and in connect and overlap high difficulty numstring identification rate reach 95.9%, the effect is good also surpasses the goal paper. We also makes a system, comes the reality to examine our achievement.
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Huang, Ya-Ling, and 黃雅鈴. "Implementation and Design an Interactive User Interface with Integration of Dynamic Gesture and Handwritten Numeral Recognition." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/81541060488532413969.
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碩士國立臺北教育大學
資訊科學系碩士班
101
The HCI (Human-Computer Interface) technology development has been an important topic becasue of the popularity of digital technology and the innovation of 3C product. The product of the NUI (Natural User Interface) is more popular than the de-sign of HCI base on a joystick, mouse, keyboard and touch. It always uses the body movements to achieve the interactive in recently years. After "Kinect" somatosensory controller was announced by the Microsoft Corporation, it open an innovative tech-nologies in the natural user interface, which allows users to get more natural, more diverse ways to interact with machine. In this paper, the Kinect controller can complete a future digital home ITV (Interactive TV) and interactive multimedia software of control that allows users to achieve the visual and interactive operation with a more intuitive and user-friendly operation. It differs from the traditional button control. And this paper can achieve the effect of interaction HCI by Kinect. The interactive control system of this paper is included both of dynamic and hand-writing recognition. The dynamic gesture uses the Kinect Sensor to achieve the real-time interaction. The handwriting digital character could be used to select TV channel. The digital character use the BPNN (Back Propagation Neural Network) to achieve the recognition and it also has a good performance in this paper.
17
Yang, Shih-Lii, and 楊世禮. "Handwritten Numeral Recognition Based on the Neural Network and Its Application in an Automatic Score Register System." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/62315500368623754715.
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碩士淡江大學
電機工程學系
85
Handwritten numeral recognition has high potential in some applications in our daily life. It can be used in a wide range of applications, such as an automatic score register system, license-plate data verification, ZIP code recognition, etc. As a result, in the recent years many researchers have proposed relevant methods and systems for handwritten numeral recognition. In this paper, the author proposed a handwritten digit recognition system based on a supervised HyperRectangular Composite Neural Network (HRCNN) and then applied this system to an automatic score register system. This system is composed of three parts: preprocessing, numeral extraction, and recognition and the author used this system in both the handwritten scores and the printed serial numbers on the examination paper. In the first stage, the image of the paper is obtained as the input and some processing is performed on the input image, such as image binarization and segmentation. In the second stage, object labeling is used to extract the connected components in the image. The connected components can be used to find the position of the serial number. In the third stage, nonlinear normalization is performed to get a normalized image for recognition. The purpose for using nonlinear normalization is to get a image with a fixed size and to adjust the density of the strokes in a adequate manner. The features using localized arc patterns are extracted from the normalized image. The features are then used as the input to the HRCNN and the recognition result can be obtained. Handwritten numerals of 70 persons were collected as the data set. Each person wrote numerals from 0 to 9 six times. Three times of these are used as the training set and the others as the testing set. A good result was obtained for this data set. Another 80 examination papers were used for testing. These papers were collected from four teachers and each teacher provided 20 papers. The recognition rate in the serial numbers is 100% since the numerals are printed numbers. On the other hand, in the handwritten scores, a recognition rate of 93.75% was obtained.
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Zhou, Jie. "Recognition and verification of unconstrained handwritten numerals." Thesis, 1999. http://spectrum.library.concordia.ca/959/1/NQ47716.pdf.
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Despite the success of many recognition systems for handwritten numerals within constrained domains, the problem remains difficult when unconstrained inputs are involved. The gap between the state-of-the-art machine recognition reliability and high practical demand leads to this investigation of verification scheme in pattern recognition. A pattern verifier is an expert specially trained to reliably confirm or negate a pattern identity from the General Purpose Recognizer (GPR), with the intention to significantly improve the class-specific Precision Rates of the system. The main goal of this thesis is to study the promising and critical role of a verifier in a recognition system. Theoretical aspects of a verifier including its unique task and functionality, inherent requirement, evaluation measurement, design concern and control strategy are discussed throughout the thesis, focusing on the problems of recognizing Unconstrained Isolated Handwritten Numerals (UIHN) and Unconstrained Touching Handwritten Numerals (UTHN). For each problem, an integrated recognition and verification system is designed and evaluated by incorporating together the GPR and the verifier. The GPR for UIHN is a combination of three conventional neural approaches. In the design of class-specific verifier for UIHN, a new kind of neural network--Quantum Neural Network (QNN)--with better distinguishing ability along decision boundary, is embedded in an efficient way. Novel experiments have been designed for in-depth studies of applying the QNN to both real data and confusing images synthesized by morphing. CENPARMI database and MNIST database are used for evaluation. UTHN recognition is an important component for automatic document processing in applications such as cheque processing. However, it is a more difficult problem that has attained less attention, reflected by the mediocre performance of current systems and lack of benchmarking databases. Two databases IRIS-Bell'98 and NIST for UTHN are newly built by the researchers at CENPARMI and the author. They are used in this research and are intended to serve as standard databases in this field. A novel graph-based combination of segmentation and recognition schemes is used in GPR for UTHN. Effective domain specific strategies making use of touching type, touching location and structural information are applied in the verifier for UTHN. The recognition and verification system for UIHN achieved a precision rate of 99.1% on MNIST database while the one for UTHN reached a precision rate of 96.1% on NIST database. The two systems are also evaluated by hypothesis testing. The substantial improvement of system precision rates by verification scheme proves the effectiveness of the proposed systems and justifies the important role of verifiers in the OCR system.
19
Scattolin, Patrice. "Recognition of handwritten numerals using elastic matching." Thesis, 1993. http://spectrum.library.concordia.ca/4081/1/MM90895.pdf.
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Dai, Weiqian. "Recognition of handwritten numerals using neural networks." Thesis, 1990. http://spectrum.library.concordia.ca/4082/1/MM64673.pdf.
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Hsiao, Tai-Yuan, and 蕭泰源. "Uncertain Classification Decision for Handwritten Numerals Recognition." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/30689272295921333832.
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碩士淡江大學
資訊管理學系
87
A uncertain classification decision system for handwritten numerals recognition is proposed. A thinning algorithm is first used to obtain the skeleton of a numeral. A set of feature points are then detected. The skeleton can be decomposed into geometric primitives. Finally, we use the relationships between the primitives to recognize the numeral. For the digits which have the similar primitives, a membership function is used to decide the probability of each digit. The handwritten numerals extracted from the NIST Special Database 19 are used to test the system. The correct rate is 88.72%.
22
陳雙喜. "Recognition of handwritten capital letters & numerals." Thesis, 1989. http://ndltd.ncl.edu.tw/handle/71272503961805889720.
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Tan, Yu. "Recognition of ambiguous pairs of totally unconstrained handwritten numerals." Thesis, 2002. http://spectrum.library.concordia.ca/1584/1/MQ68480.pdf.
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In order to improve the recognition rate of totally unconstrained handwritten numerals, a verification stage is added after classification to distinguish the ambiguous numerals between two or more classes. Two recognition methods, structural method and statistical method, are used to overcome the limitations of a single method and deliver a much more reliable recognition system. The recognition rate is 71.32% using structural method without verification. The verification stage improves the recognition rate from 71.32% to 87.50%. After combining the structural method with statistical method, the final recognition rate reached up to 95.59%
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Jean, Lai Bor, and 賴柏均. "The Study of Recognition Systems for Handwritten Touched Numerals." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/47067999538237825841.
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碩士國立交通大學
資訊工程研究所
83
This Thesis develops segmentation techniques for touching numeral strings.The segmentation of touching numeral strings has been known difficulty problems, due to the simularity between the touching point and the join point of normal numerals.In addition, touching and broken strokes as well as distortion characters make the proper segmentation even more difficulty.In this research, we propose a new concept for this matter: proper segmentation needs to be interactive with numeral reconition process. Therefore, we develop a system contains three parts: (1) segmentation subsystem, (2) understanding subsystems, and (3) recognition subsystem. The segmentation subsystem performs touching detecting and cutting processes. A neural network and an expert system are designed to understand and to recognize segmented numeral string. Based on our experience we are able to cut and to recognize touching numeral string with 80.67% of correct rate. In order to be compatible and compitible with other system, we use the numeral data base published by CEDAR of SUNY for either system training and testing.
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DAI, MIN-LUN, and 戴敏倫. "Neural network based handwritten alpha-numeric recognition system." Thesis, 1990. http://ndltd.ncl.edu.tw/handle/79069982769662199652.
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"An Integrated architecture for recognition of totally unconstrained handwritten numerals." Productivity From Information Technology, "PROFIT" Research Initiative, Sloan School of Management, Massachusetts Institute of Technology, 1993. http://hdl.handle.net/1721.1/2547.
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Amar Gupta ... [et al.]Reprint. Reprinted from the International journal of pattern recognition and artificial intelligence. Vol. 7, no. 4 (1993) "January 1993."
Includes bibliographical references (p. 127-128).
Supported by the Productivity From Information Technology (PROFIT) Research Initiative at MIT.
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Huang, Yea-Shuan. "Combination of multiple classifiers for the recognition of totally unconstrained handwritten numerals." Thesis, 1994. http://spectrum.library.concordia.ca/2732/1/NN01287.pdf.
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Uses a new approach, called the "Combination of multiple experts", to the problem of recognition of handwritten numerals. This thesis focuses on methodologies which leads to effective decision combination schemes.
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La, Tien Dung. "A cluster-based classification method for the recognition of unconstrained handwritten numerals." Thesis, 1985. http://spectrum.library.concordia.ca/4459/1/ML23155.pdf.
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