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Text Detection and Recognition in Imagery: A Survey.
IEEE Transactions on Pattern Analysis and Machine Intelligence
|September 10, 2015
Summary
This review compares text detection and recognition methods for color images, highlighting challenges and performance. It categorizes techniques and addresses issues like degraded or distorted text for better visual data analysis.
Area of Science:
- Computer Vision
- Image Processing
- Artificial Intelligence
Background:
- Text detection and recognition in color imagery is crucial for various applications.
- Existing research faces challenges in handling diverse text types and image conditions.
Purpose of the Study:
- To analyze and compare technical challenges, methods, and performance in text detection and recognition research for color imagery.
- To provide a comprehensive overview of existing techniques and identify key factors for addressing fundamental problems.
Main Methods:
- Categorization of existing techniques into stepwise and integrated approaches.
- Highlighting sub-problems: text localization, verification, segmentation, and recognition.
- Addressing special issues: enhancement of degraded text, video text, multi-oriented, distorted, and multilingual text.
Main Results:
- Enumeration of factors to consider when addressing text detection and recognition problems.
- Illustration of text categories and sub-categories.
- Comparison of the performance of representative approaches on benchmark datasets.
Conclusions:
- The review provides a fundamental analysis of the current state and remaining problems in text detection and recognition for color imagery.
- It serves as a guide for researchers and practitioners in the field.
