Image Processing Based Scene-Text Detection and Recognition with Tesseract

TL;DR

This paper presents a method for scene-text detection and recognition in natural images using image processing and Tesseract OCR, achieving over 80% character recognition accuracy in a practical, low-power setup.

Contribution

It introduces a pipeline combining image processing with Tesseract OCR for scene-text detection in natural images, addressing false positives and practical deployment challenges.

Findings

Achieved over 80% character recognition accuracy.

Identified and addressed false positive detection issues.

Demonstrated feasibility of low-power, real-time scene-text recognition.

Abstract

Text Recognition is one of the challenging tasks of computer vision with considerable practical interest. Optical character recognition (OCR) enables different applications for automation. This project focuses on word detection and recognition in natural images. In comparison to reading text in scanned documents, the targeted problem is significantly more challenging. The use case in focus facilitates the possibility to detect the text area in natural scenes with greater accuracy because of the availability of images under constraints. This is achieved using a camera mounted on a truck capturing likewise images round-the-clock. The detected text area is then recognized using Tesseract OCR engine. Even though it benefits low computational power requirements, the model is limited to only specific use cases. This paper discusses a critical false positive case scenario occurred while testing and elaborates the strategy used to alleviate the problem. The project achieved a correct character recognition rate of more than 80\%. This paper outlines the stages of development, the major challenges and some of the interesting findings of the project.