From pixels to paragraphs: The use of contextual models in text recognition

Several hierarchical levels in modeling text are discussed. At each level, the model is typically based on elements at the next lower level, e.g., characters in terms of features, words in terms of characters, phrases/sentences in terms of words, and paragraphs in terms of sentences. Such models are referred to as analytic, since they are based on step-by-step analysis. Although such models are usually sufficient, in order to achieve system robustness it is necessary to use models that skip a level, e.g., words in terms of features/pixels and sentences in terms of characters. Such models are holistic in that decisions about entities are made in the context of decisions about other entities. Holistic methods are slower than analytic methods and do not necessarily perform better. However, when analytic and holistic methods are combined, overall system performance is higher than either alone. Recognition algorithms based on different models can be combined to achieve robustness