SCFlow

This repository provides the OCR(C++) & SCD(MATLAB) implementation for the following paper:

Structured Cluster Detection from Local Feature Learning for Text Region Extraction
Huei-Yung Lin and Chin-Yu Hsu In MDPI 2023.

Abstract: The detection of interested regions is commonly considered as an early stage of information extraction from images. It is used to provide the contents meaningful to the human perception for machine vision applications. In this work, a new technique for structured region detection based on the distillation of local image features with clustering analysis is proposed. Different from the existing methods, our approach takes the application-specific reference images for feature learning and extraction. It is able to identify the text clusters under the sparsity of feature points derived from the characters. For the localization of structured regions, the cluster with a high feature density is calculated and served as a candidate for region expansion. An iterative adjustment is then performed to enlarge the ROI for complete text coverage. The experiments carried out for text region detection of invoice and banknote have demonstrated the effectiveness of the proposed technique.

Features

Local Feature Learning

Structured Cluster Detection

Getting Started

0. Requirements

• Visual Studio 2008
• OpenCV 2.1
• MATLAB

1. Optical Character Recognition

Training

• Move any subfolder under trainingdata directory.
• Build and run the WindowsForm GUI of training project.
• Click Load Image to multi-select images.
• Click button Train to start.

Inference

• Build and run the inference project.
• Click Load Image to choose image which want to recognize text.
• Click button Recognize.

2. Structured Cluster Detection

• Add test samples under the test sample/ directory.
• Run initial.m to extract the local features.
• Run optics_clustering.m to clustering found local features on target image.
• Run main.m to identify and output the target ROI to output folder.

Acknowledgments

Code borrows heavily from Neural Network OCR. Thanks for the sharing.