minhnhat2001vt / Scence-Text-Recognition-With-YOLOv8-and-CRNN

This is an implementation of YOLOv8 and CRNN network for Scene Text Recognition task

Scence Text Recognition With YOLOv8 and CRNN

Scene Text Recognition (STR) is a problem that applies image processing and character recognition algorithms to identify text appearing in images. A Scene Text Recognition program usually includes two main stages: Text Detection (Detector) and Text Recognition (Recognizer).

The goal of this project is to build a STR model that can takes an image containing text as input, then returns Coordinate location and text in the image as outputs.

1. Dataset

• The dataset using in this project is ICDAR 2003, which is a dataset for scene text recognition.
• Contains 507 natural scene images, (including 258 training images and 249 test images) in total.
• Dataset download at: https://drive.google.com/file/d/1x9e2FNDlKc_lBkJvHvWSKKfCSSqNsQfM/view

2. Data Preparation

3. Text Detector with YOLOv8

• Model summary: YOLOv8 is an advanced object detection model by Ultralytics that uses anchor-free detection
• Training:

4. Text Recognizer with CRNN

• Model summary:
• Training:

5. Evaluation

• YOLOv8:
• Plain CRNN:
• CRNN + Skip Connection:
• CRNN + Resnet

6. References

• 7th International Conference on Document Analysis and Recognition (ICDAR 2003), 2-Volume Set, 3-6 August 2003, Edinburgh, Scotland, UK. IEEE Computer Society 2003, ISBN 0-7695-1960-1