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Topic Language Model Adaption for Recognition of Homologous Offline Handwritten Chinese Text Image
2014 / IEEE
This item from - IEEE Letter - Signal Processing and Analysis - As the content of a full text page usually focuses on a specific topic, a topic language model adaption method is proposed to improve the recognition performance of homologous offline handwritten Chinese text image. Firstly, the text images are recognized with a character based bi-gram language model. Secondly, the topic of the text image is matched adaptively. Finally, the text image is recognized again with the best matched topic language model. To obtain a tradeoff between the recognition performance and computational complexity, a restricted topic language model adaption method is further presented. The methods have been evaluated on 100 offline Chinese text images. Compared to the general language model, the topic language model adaption has reduced the relative error rate by 11.94%. The restricted topic language model has lessened the running time by 19.22% at the cost of losing 0.35% of the accuracy.
Over-segmentation And Merging
Topic Language Model
Character Based Bi-gram
Offline Handwritten Chinese Text Image Recognition
Signal Processing And Analysis