Enhancing the Mongolian Historical Document Recognition System with Multiple Knowledge-Based Strategies

摘要

This paper describes recent work on integrating multiple strategies to improve the performance of the Mongolian historical document recognition system which utilize the segmentation-based scheme. We analyze the reasons why the recognition errors happened. On such basis, we propose three strategies according to the knowledge of the glyph characteristics of Mongolian and integrate them into glyph-unit recognition. The strategies are recognizing the under-segmented and over-segmented fragments (RUOF), glyph-unit grouping (GG) and incorporating the baseline information (IBI). The first strategy helps in correcting the segmentation error and the remaining two strategies further improve the classifiers accuracies. The experiment on the historical Mongolian Kanjur demonstrates that utilizing these strategies could effectively increase the accuracy of word recognition.