Design of an Optical Character Recognition System for Camera-based Handheld Devices

TL;DR

This paper develops an OCR system optimized for camera-captured text on handheld devices, achieving high accuracy and efficiency suitable for mobile applications.

Contribution

It introduces a complete OCR pipeline tailored for camera images on handheld devices, with improved accuracy and low resource consumption.

Findings

Achieved 92.74% recognition accuracy on business card images

The system is computationally efficient and low memory

Outperforms Tesseract in mobile scenarios

Abstract

This paper presents a complete Optical Character Recognition (OCR) system for camera captured image/graphics embedded textual documents for handheld devices. At first, text regions are extracted and skew corrected. Then, these regions are binarized and segmented into lines and characters. Characters are passed into the recognition module. Experimenting with a set of 100 business card images, captured by cell phone camera, we have achieved a maximum recognition accuracy of 92.74%. Compared to Tesseract, an open source desktop-based powerful OCR engine, present recognition accuracy is worth contributing. Moreover, the developed technique is computationally efficient and consumes low memory so as to be applicable on handheld devices.