A Hough Transform based Technique for Text Segmentation

TL;DR

This paper presents a Hough transform-based method for segmenting text lines and words in images, applicable to various domains including documents, business cards, and license plates, demonstrating high accuracy across datasets.

Contribution

The work introduces a novel application of Hough transform for robust text segmentation across multiple image types and scripts, including handwritten and printed text.

Findings

Achieved 85.7% word segmentation accuracy on document images.

Achieved 94.6% word segmentation accuracy on business card images.

Achieved 88% word segmentation accuracy on surveillance camera images.

Abstract

Text segmentation is an inherent part of an OCR system irrespective of the domain of application of it. The OCR system contains a segmentation module where the text lines, words and ultimately the characters must be segmented properly for its successful recognition. The present work implements a Hough transform based technique for line and word segmentation from digitized images. The proposed technique is applied not only on the document image dataset but also on dataset for business card reader system and license plate recognition system. For standardization of the performance of the system the technique is also applied on public domain dataset published in the website by CMATER, Jadavpur University. The document images consist of multi-script printed and hand written text lines with variety in script and line spacing in single document image. The technique performs quite satisfactorily when applied on mobile camera captured business card images with low resolution. The usefulness of the technique is verified by applying it in a commercial project for localization of license plate of vehicles from surveillance camera images by the process of segmentation itself. The accuracy of the technique for word segmentation, as verified experimentally, is 85.7% for document images, 94.6% for business card images and 88% for surveillance camera images.