Crossing the lines: making optimal use of context in line-based Handwritten Text Recognition

Hand-written text recognition (HTR) is often carried out line-by-line: the decoding of text lines is carried out independently. This approach is known to deteriorate recognition accuracy of words and characters close to the line boundaries. The present study investigates this issue from the point of view of the language modeling component of the HTR system. Obviously, lack of linguistic context may be one of the reasons for loss of accuracy, but it certainly is not the only factor in play. We seek to clarify to which extent the problem can be influenced by the language modeling component of the system. We first discuss how to develop adapted language models which significantly improve HTR performance in general. We then focus on the deployment of methods to improve accuracy at line boundaries. The final result is an efficient approach which significantly improves HTR accuracy without changing the basic HTR system setup.