A Novel Transcript Mapping Technique for Handwritten Document Images

Transcript mapping refers to the process of aligning meaningful units of a handwritten document image (e.g. Text lines, words, characters) with the corresponding transcription information. It has many applications such as (i) fast generation of ground truth at different granularity levels and (ii) indexing handwritten collections for document retrieval. In this paper, a novel transcript mapping technique is proposed which is guided by the number of words as well as the characters per word of a text line. The proposed method combines the results of a local and a global approach using a scoring algorithm. The efficiency of the proposed method is demonstrated by experimentation conducted on a known, publicly available dataset, achieving word level alignment accuracy of 99.48%.