Applications of Recurrent Neural Network Language Model in Offline Handwriting Recognition and Word Spotting
Nan Li, Jinying Chen, Huaigu Cao, Bing Zhang, Prem Natarajan · 2014
The recurrent neural network language model (RNNLM) is a discriminative, non-Markovian model that can capture long-span word history in natural language. It has been proved to be successful in automatic speech recognition and machine translation. In this work, we applied RNNLM to the n-best rescoring stage of the state-of-the-art BBN Byblos OCR (optical character recognition) system for handwriting recognition.1 With RNNLM scores as additional features, our system achieved significant improvement (p <; 0.001), a 3.5% relative reduction on OCR word error rate, compared with a high baseline that uses n-gram language model for rescoring. We have also developed a novel method to integrate the OCR n-best RNNLM scores into the word posterior probabilities in OCR confusion networks, which resulted in consistent observable improvements in word spotting for OCR'ed handwritten documents, as measured by both mean average precision (MAP) and detection-error tradeoff (DET) curves.