Topic Language Model Adaption for Recognition of Homologous Offline Handwritten Chinese Text Image

Abstract

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.Index Terms-Character based bi-gram, offline handwritten Chinese text image recognition, over-segmentation and merging, topic language model.

Text Character Extraction Implementation from Captured Handwritten Image to Text Conversionusing Template Matching Technique

“…In this paper used algorithm is Optical Character Recognition (OCR).Advantage is decrease some possible human errors and high speed of recognition. Disadvantage is multiple font and size characters and handwritten characters are not recognize (Faisal Mohammad,Jyoti Anarase,Milan Shingote and Pratik Ghanwat) [3].…”

“…In case of offline character recognition the handwritten character is typically scanned in form of a paper document and made available in the form of a gray scaleimage to the recognition algorithms [3].…”