MAIL SORTER USING LABVIEW

The Indian postal system is the largest networks in the world. Being the 7th largest country in the world, major population of the country is rural based, where the basic amenities of life is a sweet dream. In such a scenario, having an efficient mail delivery system is essential. Hence, to eliminate the drawbacks in other processes, we propose to fully automate the sorting process. Unlike the code generation technique, it neither requires any human intervention to generate a code based on the pin code nor will be a problem in case of absence of the pin code. The principle used for sorting is the Optical Character Recognition using LabVIEW software. Camera, placed over the slide unit captures the image of the address. The pin code or the state (in the absence of the pin code) is selected and compared with a set of trained characters in the data base. On finding a positive match, based on the first two digits of the pin code or the first four letters of the state, the mail is segregated by the LabVIEW program involving OCR technique. The processed data is sent to the real time application by the DAQ card, which activates the actuating arm(servo motor) to allow the letters to move to the respective stack(zone) and thus sorting the mails automatically, reducing the human effort and errors

Character Recognition

Character recognition is one of the pattern recognition technologies that are most widely used in practical applications. This book presents recent advances that are relevant to character recognition, from technical topics such as image processing, feature extraction or classification, to new applications including human-computer interfaces. The goal of this book is to provide a reference source for academic research and for professionals working in the character recognition field

Fuzzy Logic Classification of Handwritten Signature Based Computer Access and File Encryption

Often times computer access and file encryption is successful based on how complex a password will be, how often users could change their complex password, the length of the complex password and how creative users are in creating a complex passsword to stand against unauthorized access to computer resources or files. This research proposes a new way of computer access and file encryption based on the fuzzy logic classification of handwritten signatures. Feature extraction of the handwritten signatures, the Fourier transformation algorithm and the k-Nearest Algorithm could be implemented to determine how close the signature is to the signature on file to grant or deny users access to computer resources and encrypted files. lternatively implementing fuzzy logic algorithms and fuzzy k-Nearest Neighbor algorithm to the captured signature could determine how close a signature is to the one on file to grant or deny access to computer resources and files. This research paper accomplishes the feature recognition firstly by extracting the features as users sign their signatures for storage, and secondly by determining the shortest distance between the signatures. On the other hand this research work accomplish the fuzzy logic recognition firstly by classifying the signature into a membership groups based on their degree of membership and secondly by determining what level of closeness the signatures are from each other. The signatures were collected from three selected input devices- the mouse, I-Pen and the IOGear. This research demonstrates which input device users found efficient and flexible to sign their respective names. The research work also demonstrates the security levels of implementing the fuzzy logic, fuzzy k-Nearest Neighbor, Fourier Transform.Master'sCollege of Arts and Sciences: Computer ScienceUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/117719/1/Kwarteng.pd

Integration of traditional imaging, expert systems, and neural network techniques for enhanced recognition of handwritten information

Includes bibliographical references (p. 33-37).Research supported by the I.F.S.R.C. at M.I.T.Amar Gupta, John Riordan, Evelyn Roman

Optical Character Recognition Using Morphological Attributes.

This dissertation addresses a fundamental computational strategy in image processing hand written English characters using traditional parallel computers. Image acquisition and processing is becoming a thriving industry because of the frequent availability of fax machines, video digitizers, flat-bed scanners, hand scanners, color scanners, and other image input devices that are now accessible to everyone. Optical Character Recognition (OCR) research increased as the technology for a robust OCR system became realistic. There is no commercial effective recognition system that is able to translate raw digital images of hand written text into pure ASCII. The reason is that a digital image comprises of a vast number of pixels. The traditional approach of processing the huge collection of pixel information is quite slow and cumbersome. In this dissertation we developed an approach and theory for a fast robust OCR system for images of hand written characters using morphological attribute features that are expected by the alphabet character set. By extracting specific morphological attributes from the scanned image, the dynamic OCR system is able to generalize and approximate similar images. This generalization is achieved with the usage of fuzzy logic and neural network. Since the main requirement for a commercially effective OCR is a fast and a high recognition rate system, the approach taken in this research is to shift the recognition computation into the system\u27s architecture and its learning phase. The recognition process constituted mainly simple integer computation, a preferred computation on digital computers. In essence, the system maintains the attribute envelope boundary upon which each English character could fall under. This boundary is based on extreme attributes extracted from images introduced to the system beforehand. The theory was implemented both on a SIMD-MC\sp2 and a SISD machine. The resultant system proved to be a fast robust dynamic system, given that a suitable learning had taken place. The principle contributions of this dissertation are: (1) Improving existing thinning algorithms for image preprocessing. (2) Development of an on-line cluster partitioning procedure for region oriented segmentation. (3) Expansion of a fuzzy knowledge base theory to maintain morphological attributes on digital computers. (4) Dynamic Fuzzy learning/recognition technique

HOW TO OVERCOME ANALPHABETISM IN READING CHINESE CHARACTER

Most languages in the world use some system of alphabetical characters: Latin, Greek, Cyrillic, Hebrew, Arabic, Hindi and so on. A foreigner that does not know the language can get some information from a written text provided he knows the alphabet and disposes of a dictionary. When there is no longer an alphabet, but only pictorial characters, the problem becomes at first unsolvable. Chinese is the main language where an ignorant foreigner is completely analphabet. Fortunately there are methods that after some training allow the recognition of pictorial characters. In our university some twenty pupils of the Excellence School participated to an experiment of Chinese alphabetization gluing a traditional practical Chinese first course with information theory methods for dealing with image data bases. In this article first we discuss both the theoretical foundations. Then we give a report of the merging of the two conceptual schemes as it was performed at the excellence school. Finally we draw some conclusions about improvements of the method

Advanced document data extraction techniques to improve supply chain performance

In this thesis, a novel machine learning technique to extract text-based information from scanned images has been developed. This information extraction is performed in the context of scanned invoices and bills used in financial transactions. These financial transactions contain a considerable amount of data that must be extracted, refined, and stored digitally before it can be used for analysis. Converting this data into a digital format is often a time-consuming process. Automation and data optimisation show promise as methods for reducing the time required and the cost of Supply Chain Management (SCM) processes, especially Supplier Invoice Management (SIM), Financial Supply Chain Management (FSCM) and Supply Chain procurement processes. This thesis uses a cross-disciplinary approach involving Computer Science and Operational Management to explore the benefit of automated invoice data extraction in business and its impact on SCM. The study adopts a multimethod approach based on empirical research, surveys, and interviews performed on selected companies.The expert system developed in this thesis focuses on two distinct areas of research: Text/Object Detection and Text Extraction. For Text/Object Detection, the Faster R-CNN model was analysed. While this model yields outstanding results in terms of object detection, it is limited by poor performance when image quality is low. The Generative Adversarial Network (GAN) model is proposed in response to this limitation. The GAN model is a generator network that is implemented with the help of the Faster R-CNN model and a discriminator that relies on PatchGAN. The output of the GAN model is text data with bonding boxes. For text extraction from the bounding box, a novel data extraction framework consisting of various processes including XML processing in case of existing OCR engine, bounding box pre-processing, text clean up, OCR error correction, spell check, type check, pattern-based matching, and finally, a learning mechanism for automatizing future data extraction was designed. Whichever fields the system can extract successfully are provided in key-value format.The efficiency of the proposed system was validated using existing datasets such as SROIE and VATI. Real-time data was validated using invoices that were collected by two companies that provide invoice automation services in various countries. Currently, these scanned invoices are sent to an OCR system such as OmniPage, Tesseract, or ABBYY FRE to extract text blocks and later, a rule-based engine is used to extract relevant data. While the system’s methodology is robust, the companies surveyed were not satisfied with its accuracy. Thus, they sought out new, optimized solutions. To confirm the results, the engines were used to return XML-based files with text and metadata identified. The output XML data was then fed into this new system for information extraction. This system uses the existing OCR engine and a novel, self-adaptive, learning-based OCR engine. This new engine is based on the GAN model for better text identification. Experiments were conducted on various invoice formats to further test and refine its extraction capabilities. For cost optimisation and the analysis of spend classification, additional data were provided by another company in London that holds expertise in reducing their clients' procurement costs. This data was fed into our system to get a deeper level of spend classification and categorisation. This helped the company to reduce its reliance on human effort and allowed for greater efficiency in comparison with the process of performing similar tasks manually using excel sheets and Business Intelligence (BI) tools.The intention behind the development of this novel methodology was twofold. First, to test and develop a novel solution that does not depend on any specific OCR technology. Second, to increase the information extraction accuracy factor over that of existing methodologies. Finally, it evaluates the real-world need for the system and the impact it would have on SCM. This newly developed method is generic and can extract text from any given invoice, making it a valuable tool for optimizing SCM. In addition, the system uses a template-matching approach to ensure the quality of the extracted information

Advances in Character Recognition

This book presents advances in character recognition, and it consists of 12 chapters that cover wide range of topics on different aspects of character recognition. Hopefully, this book will serve as a reference source for academic research, for professionals working in the character recognition field and for all interested in the subject