A Low Computational Approach for Price Tag Recognition

TL;DR

This paper presents a resource-efficient method for detecting and recognizing price tags in photographs taken with small cameras, using binarization and connected component analysis tailored to known geometries.

Contribution

It introduces a low computational approach combining Niblack binarization and geometric modeling for price tag recognition on resource-constrained devices.

Findings

High recognition quality on private dataset

Effective in resource-limited environments

Applicable to small-scale digital camera images

Abstract

In this work we discuss the task of search, localization and recognition of price zone within a photograph of the price tag. The task is being addressed for the case when image is acquired by small-scale digital camera and calculation device has significant resource constraints. The proposed approach is based on Niblack binarization algorithm, analysis and clasterization of connected components in conditions of known price tag geometrical model. The algorithm was tested on a private dataset and has shown high quality.