A Syntactic Omni-Font Character Recognition System

The author introduces a syntactic omni-font character recognition system that recognizes a wide range of fonts, including handprinted characters. A structural pattern-matching approach is used. Essentially, a set of loosely constrained rules specify pattern components and their interrelationships. The robustness of the system is derived from the orthogonal set of pattern descriptors, location functions, and the manner in which they are combined to exploit the topological structure of characters. By virtue of the new pattern description language, PDL, the user may easily write rules to define new patterns for the system to recognize. The system also features scale-invariance and user-definable sensitivity to tilt orientation. The system has achieved a 95. 2% recognition rate

Parametric classification in domains of characters, numerals, punctuation, typefaces and image qualities

This thesis contributes to the Optical Font Recognition problem (OFR), by developing a classifier system to differentiate ten typefaces using a single English character ‘e’. First, features which need to be used in the classifier system are carefully selected after a thorough typographical study of global font features and previous related experiments. These features have been modeled by multivariate normal laws in order to use parameter estimation in learning. Then, the classifier system is built up on six independent schemes, each performing typeface classification using a different method. The results have shown a remarkable performance in the field of font recognition. Finally, the classifiers have been implemented on Lowercase characters, Uppercase characters, Digits, Punctuation and also on Degraded Images

Hybrid model of post-processing techniques for Arabic optical character recognition

Optical character recognition (OCR) is used to extract text contained in an image. One of the stages in OCR is the post-processing and it corrects the errors of OCR output text. The OCR multiple outputs approach consists of three processes: differentiation, alignment, and voting. Existing differentiation techniques suffer from the loss of important features as it uses N-versions of input images. On the other hand, alignment techniques in the literatures are based on approximation while the voting process is not context-aware. These drawbacks lead to a high error rate in OCR. This research proposed three improved techniques of differentiation, alignment, and voting to overcome the identified drawbacks. These techniques were later combined into a hybrid model that can recognize the optical characters in the Arabic language. Each of the proposed technique was separately evaluated against three other relevant existing techniques. The performance measurements used in this study were Word Error Rate (WER), Character Error Rate (CER), and Non-word Error Rate (NWER). Experimental results showed a relative decrease in error rate on all measurements for the evaluated techniques. Similarly, the hybrid model also obtained lower WER, CER, and NWER by 30.35%, 52.42%, and 47.86% respectively when compared to the three relevant existing models. This study contributes to the OCR domain as the proposed hybrid model of post-processing techniques could facilitate the automatic recognition of Arabic text. Hence, it will lead to a better information retrieval

A CONCEPTUAL PROLOG ENGINE FOR AUTOMATED DICTIONARY-TO-HYPERTEXT MAPPING

This article examines the possibilities of mapping the structure of classical information sources (dictionaries, ...) in to the hypertext structure. The hypertext structure enables more efficient usage of the mapped resources (enriching it with new multimedia sources, databases and knowledge bases\u27 structuring, ...). The idea is to interpret the classical information source as formal language, and it shall be demonstrated using the example of the classical dictionary, but it is nevertheless equally applicable to all (similar) types of classical information sources. The adequate technical basis for the implementation is to be seen in the logic programming language Prolog. The method, called Conceptual Prolog Engine, has been formed and developed in this environment

A CONCEPTUAL PROLOG ENGINE FOR AUTOMATED DICTIONARY-TO-HYPERTEXT MAPPING

This article examines the possibilities of mapping the structure of classical information sources (dictionaries, ...) in to the hypertext structure. The hypertext structure enables more efficient usage of the mapped resources (enriching it with new multimedia sources, databases and knowledge bases\u27 structuring, ...). The idea is to interpret the classical information source as formal language, and it shall be demonstrated using the example of the classical dictionary, but it is nevertheless equally applicable to all (similar) types of classical information sources. The adequate technical basis for the implementation is to be seen in the logic programming language Prolog. The method, called Conceptual Prolog Engine, has been formed and developed in this environment

Template Based Recognition of On-Line Handwriting

Software for recognition of handwriting has been available for several decades now and research on the subject have produced several different strategies for producing competitive recognition accuracies, especially in the case of isolated single characters. The problem of recognizing samples of handwriting with arbitrary connections between constituent characters (emph{unconstrained handwriting}) adds considerable complexity in form of the segmentation problem. In other words a recognition system, not constrained to the isolated single character case, needs to be able to recognize where in the sample one letter ends and another begins. In the research community and probably also in commercial systems the most common technique for recognizing unconstrained handwriting compromise Neural Networks for partial character matching along with Hidden Markov Modeling for combining partial results to string hypothesis. Neural Networks are often favored by the research community since the recognition functions are more or less automatically inferred from a training set of handwritten samples. From a commercial perspective a downside to this property is the lack of control, since there is no explicit information on the types of samples that can be correctly recognized by the system. In a template based system, each style of writing a particular character is explicitly modeled, and thus provides some intuition regarding the types of errors (confusions) that the system is prone to make. Most template based recognition methods today only work for the isolated single character recognition problem and extensions to unconstrained recognition is usually not straightforward. This thesis presents a step-by-step recipe for producing a template based recognition system which extends naturally to unconstrained handwriting recognition through simple graph techniques. A system based on this construction has been implemented and tested for the difficult case of unconstrained online Arabic handwriting recognition with good results