2D Grammar Extension of the CMP Mathematical Formulae On-line Recognition System

Projecte realitzat en col.laboració amb Czech Technical University in PragueIn the last years, the recognition of handwritten mathematical formulae has recieved an increasing amount of attention in pattern recognition research. However, the diversity of approaches to the problem and the lack of a commercially viable system indicate that there is still much research to be done in this area. In this thesis, I will describe the previous work on a system for on-line handwritten mathematical formulae recognition based on the structural construction paradigm and two-dimensional grammars. In general, this approach can be successfully used in the anaylysis of inputs composed of objects that exhibit rich structural relations. An important benefit of the structural construction is in not treating symbols segmentation and structural anaylsis as two separate processes which allows the system to perform segmentation in the context of the whole formula structure, helping to solve arising ambiguities more reliably. We explore the opening provided by the polynomial complexity parsing algorithm and extend the grammar by many new grammar production rules which made the system useful for formulae met in the real world. We propose several grammar extensions to support a wide range of real mathematical formulae, as well as new features implemented in the application. Our current approach can recognize functions, limits, derivatives, binomial coefficients, complex numbers and more

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