Multiple classifier fusion using the fuzzy integral.

Fusion of multiple classifier decisions is a powerful method for increasing classification rates in difficult pattern recognition problems. Researchers have found that in many applications it is better to fuse multiple relatively simple classifiers than to build a single sophisticated classifier to achieve better recognition rates. Ideally, the combination function should take advantage of the strengths of individual classifiers and of all possible subsets of classifiers, avoid their weaknesses, and use all the dynamically available knowledge about the inputs, the outputs, the classes, and the classifiers. Automatic reading of handwritten numerals is a difficult problem because of the great variations involved in the shape of the characters. In this thesis an evidence fusion technique, based on the notion of fuzzy integral is utilized to combine the results of different classifiers and realize a robust algorithm for high accuracy handwritten numeral recognition. Both source relevance as well as source evidence are utilized to achieve significant enhancements. The most important advantage of this system is that not only is the evidence combined but that the relative importance of the different sources is also considered. Various conventional and fuzzy integral based fusion methods are explained in detail and experimental results obtained are compared. A method is introduced to improve the fuzzy densities of the classifiers which would improve the fusion results. In this method we use the correction factors obtained from the performance matrices to alter the initial fuzzy densities. Experiments on handwritten numeral recognition are described and compared. These experiments show that very low error rates can be achieved by fusing several low performance classifiers.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis1999 .B45. Source: Masters Abstracts International, Volume: 39-02, page: 0558. Adviser: M. Ahmadi. Thesis (M.A.Sc.)--University of Windsor (Canada), 1999

Handwritten Digit Recognition and Classification Using Machine Learning

In this paper, multiple learning techniques based on Optical character recognition (OCR) for the handwritten digit recognition are examined, and a new accuracy level for recognition of the MNIST dataset is reported. The proposed framework involves three primary parts, image pre-processing, feature extraction and classification. This study strives to improve the recognition accuracy by more than 99% in handwritten digit recognition. As will be seen, pre-processing and feature extraction play crucial roles in this experiment to reach the highest accuracy

Off-line Thai handwriting recognition in legal amount

Thai handwriting in legal amounts is a challenging problem and a new field in the area of handwriting recognition research. The focus of this thesis is to implement Thai handwriting recognition system. A preliminary data set of Thai handwriting in legal amounts is designed. The samples in the data set are characters and words of the Thai legal amounts and a set of legal amounts phrases collected from a number of native Thai volunteers. At the preprocessing and recognition process, techniques are introduced to improve the characters recognition rates. The characters are divided into two smaller subgroups by their writing levels named body and high groups. The recognition rates of both groups are increased based on their distinguished features. The writing level separation algorithms are implemented using the size and position of characters. Empirical experiments are set to test the best combination of the feature to increase the recognition rates. Traditional recognition systems are modified to give the accumulative top-3 ranked answers to cover the possible character classes. At the postprocessing process level, the lexicon matching algorithms are implemented to match the ranked characters with the legal amount words. These matched words are joined together to form possible choices of amounts. These amounts will have their syntax checked in the last stage. Several syntax violations are caused by consequence faulty character segmentation and recognition resulting from connecting or broken characters. The anomaly in handwriting caused by these characters are mainly detected by their size and shape. During the recovery process, the possible word boundary patterns can be pre-defined and used to segment the hypothesis words. These words are identified by the word recognition and the results are joined with previously matched words to form the full amounts and checked by the syntax rules again. From 154 amounts written by 10 writers, the rejection rate is 14.9 percent with the recovery processes. The recognition rate for the accepted amount is 100 percent

Recognition-based Approach of Numeral Extraction in Handwritten Chemistry Documents using Contextual Knowledge

International audienceThis paper presents a complete procedure that uses contextual and syntactic information to identify and recognize amount fields in the table regions of chemistry documents. The proposed method is composed of two main modules. Firstly, a structural analysis based on connected component (CC) dimensions and positions identifies some special symbols and clusters other CCs into three groups: fragment of characters, isolated characters or connected characters. Then, a specific processing is performed on each group of CCs. The fragment of characters are merged with the nearest character or string using geometric relationship based rules. The characters are sent to a recognition module to identify the numeral components. For the connected characters, the final decision on the string nature (numeric or non-numeric) is made based on a global score computed on the full string using the height regularity property and the recognition probabilities of its segmented fragments. Finally, a simple syntactic verification at table row level is conducted in order to correct eventual errors. The experimental tests are carried out on real-world chemistry documents provided by our industrial partner eNovalys. The obtained results show the effectiveness of the proposed system in extracting amount fields

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