CVOct 9, 2017
A Bottom Up Procedure for Text Line Segmentation of Latin Script
arXiv:1710.03027v1
Originality Synthesis-oriented
AI Analysis
This addresses text line segmentation for document analysis, but appears incremental as it builds on existing techniques without claiming major breakthroughs.
The paper tackles the problem of segmenting text lines in Latin script documents by proposing a bottom-up procedure that combines image morphology, feature extraction, and Gaussian mixture models, with experimental results demonstrating its validity.
In this paper we present a bottom up procedure for segmentation of text lines written or printed in the Latin script. The proposed method uses a combination of image morphology, feature extraction and Gaussian mixture model to perform this task. The experimental results show the validity of the procedure.