Tin Kam Ho

Decision combination in multiple classifier systems

A multiple classifier system is a powerful solution to difficult pattern recognition problems involving large class sets and noisy input because it allows simultaneous use of arbitrary feature descriptors and classification procedures. Decisions by the classifiers can be represented as rankings of classifiers and different instances of a problem. The rankings can be combined by methods that either reduce or rerank a given set of classes. An intersection method and union method are proposed for class set reduction. Three methods based on the highest rank, the Borda count, and logistic regression are proposed for class set reranking. These methods have been tested in applications of degraded machine-printed characters and works from large lexicons, resulting in substantial improvement in overall correctness. >

On multiple classifier systems for pattern recognition

Difficult pattern recognition problems involving large class sets and noisy input can be solved by a multiple classifier system, which allows simultaneous use of arbitrary feature descriptors and classification procedures. Independent decisions by each classifier can be combined by methods of the highest rank, Borda count, and logistic regression, resulting in substantial improvement in overall correctness.<<ETX>>

A word shape analysis approach to lexicon based word recognition

Abstract A method for word recognition is presented that is based on an analysis of the shape of a word as a whole object. It is demonstrated to be a useful alternative for recognizing degraded word images that are prone to errors in character segmentation.

A computational model for recognition of multifont word images

A computational model for the recognition of multifont machine-printed word images of highly variable quality is given. The model integrates three word-recognition algorithms, each of which utilizes a different form of shape and context information. The approaches are character-recognition-based, segmentation-based, and word-shape-analysis based. The model overcomes limitations of previous solutions that focus on isolated characters. In an experiment using a lexicon of 33,850 words and a test set of 1,671 highly variable word images, the algorithm achieved a correct rate of 89% at the top choice and 95% in the top ten choices.

Combination of Decisions by Multiple Classifiers

A technique for combining the results of classifier decisions in a multi-classifier recognition system is presented. Each classifier produces a ranking of a set of classes. The combination technique uses these rankings to determine a small subset of the set of classes that contains the correct class. A group consensus function is then applied to re-rank the elements in the subset. This methodology is especially suited for recognition systems with large numbers of classes where it is valuable to reduce the decision problem to a manageable size before making a final determination about the identity of the image. Experimentation is discussed in which the proposed method is used with a word recognition problem where 40 classifiers are applied to degraded machine-printed word images and where a typical lexicon contains 235 words. A 96.6% correct rate is achieved within the 10 best decisions for 817 test images.

World image matching as a technique for degraded text recognition

A technique is presented that determines equivalences between word images in a passage of text. A clustering procedure is applied to group visually similar words. Initial hypotheses for the identities of words are then generated by matching the word groups to language statistics that predict the frequency at which certain words will occur. This is followed by a recognition step that assigns identifications to the images in the clusters. This paper concentrates on the clustering algorithm. A clustering technique is presented and its performance on a running text of 1062 word images is determined. It is shown that the clustering algorithm can correctly locate groups of short function words with better than a 95 percent correct rate.<<ETX>>

A hypothesis testing approach to word recognition using dynamic feature selection

A top-down approach to word recognition is proposed. Discussions are presented on dynamically selecting the most effective feature combinations, which are applied to discriminate between a limited set of word hypotheses.<<ETX>>

Regression approach to combination of decisions by multiple character recognition algorithms

A regression method is proposed to combine decisions of multiple character recognition algorithms. The method computes a weighted sum of the rank scores produced by the individual classifiers and derive a consensus ranking. The weights are estimated by a logistic regression analysis. Two experiments are discussed where the method was applied to recognize degraded machine-printed characters and handwritten digits. The results show that the combination outperforms each of the individual classifiers.

Contextual analysis of machine-printed addresses

The assignment of a nine digit ZIP Code (ZIP + 4 Code) to the digital image of a machine printed address block is a problem of central importance in automated mail sorting. This problem is especially difficult since most addresses do not contain ZIP + 4 Codes and often the information that must be read to match an address to one of the 28 million entries in the ZIP + 4 file is either erroneous, incomplete, or missing altogether. This paper discusses a system for interpreting a machine printed address and assigning a ZIP + 4 Code that uses a constraint satisfaction approach. Words in an address block are first segmented and parsed to assign probable semantic categories. Word images are then recognized by a combination of digit, character, and word recognition algorithms. The control structure uses a constraint satisfaction problem solving approach to match the recognition results to an entry in the ZIP + 4 file. It is shown how this technique can both determine correct responses as well as compensate for incomplete or erroneous information. Experimental results demonstrate the success of this system. In a recent test on over 1000 machine printed address blocks, the ZIP + 4 encode rate was over 73 percent. This compares to the success rate of current postal OCRs which is about 45 percent. Additionally, the word recognition algorithm recognizes over 92 percent of the input images (over 98 percent in the top 10 choices.