Offline handwritten text line recognition is a hard task that requires both an efficient optical character recognizer and language model. Handwriting recognition state of the art methods are based on Long Short Term Memory (LSTM) recurrent neural networks (RNN) coupled with the use of linguistic knowledge. Most of the proposed approaches in the literature focus on improving one of the two components and use constraint, dedicated to a database lexicon. However, state of the art performance is achieved by combining multiple optical models, and possibly multiple language models with the Recognizer Output Voting Error Reduction (ROVER) framework. Though handwritten line recognition with ROVER has been implemented by combining only few recognizers because training multiple complete recognizers is hard. In this paper we propose a Lexicon Verified ROVER: LV-ROVER, that has a reduce complexity compare to the original one and that can combine hundreds of recognizers without language models. We achieve state of the art for handwritten line text on the RIMES dataset.
Although state-of-the art systems now provide interesting performance @cite_6 , the idea of combining several recognizers has emerged, in order to reach better performance. However, combining the output of several recognizers is not trivial, mainly due to the dynamical nature of the models, that may lead to output signals of different sizes. The most popular combination method is the ROVER method that aligns results of many systems (each operating a lexicon and language model driven decoding scheme), and votes for the most probable solution. ROVER has become a popular solution introduced in many methods of the literature or with some modification . We now present the ROVER combination scheme in the next paragraph.
@cite_6
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Abstract : In this paper it is shown that a combination of the classical Lagrange multiplier formalism and the functional equation technique of dynamic programming enables a number of types of variational problems involving the computation and tabulation of functions of M variables to be treated by computing first sequences of functions of K variables, and then sequences of functions of M--K variables, where K may be chosen within the range 1 or = K or = M-1. The choice of K depends upon the process This reduction in the dimensionality of the functions involved is equivalent to an increase in the capability of modern digital computers as far as dynamic programming processes are concerned.