https://www.comp.nus.edu.sg/~cs4243/projects/TextDetectionSurvey.pdf

Text information extraction in images and video: a survey

Text that appears in a scene or is graphically added to video can provide an important supplemental source of index information as well as clues for decoding the video's structure and for classification. In this work, we present algorithms for detecting and tracking text in digital video. Our system implements a scale-space feature extractor that feeds an artificial neural processor to detect text blocks. Our text tracking scheme consists of two modules: a sum of squared difference (SSD) based module to find the initial position and a contour-based module to refine the position. Experiments conducted with a variety of video sources show that our scheme can detect and track text robustly.

Automatic text detection and tracking in digital video

The increasing availability of high-performance, low-priced, portable digital imaging devices has created a tremendous opportunity for supplementing traditional scanning for document image acquisition. Digital cameras attached to cellular phones, PDAs, or wearable computers, and standalone image or video devices are highly mobile and easy to use; they can capture images of thick books, historical manuscripts too fragile to touch, and text in scenes, making them much more versatile than desktop scanners. Should robust solutions to the analysis of documents captured with such devices become available, there will clearly be a demand in many domains. Traditional scanner-based document analysis techniques provide us with a good reference and starting point, but they cannot be used directly on camera-captured images. Camera-captured images can suffer from low resolution, blur, and perspective distortion, as well as complex layout and interaction of the content and background. In this paper we present a survey of application domains, technical challenges, and solutions for the analysis of documents captured by digital cameras. We begin by describing typical imaging devices and the imaging process. We discuss document analysis from a single camera-captured image as well as multiple frames and highlight some sample applications under development and feasible ideas for future development.

Camera-based analysis of text and documents: a survey

Recognition of text in natural scene images is becoming a prominent research area due to the widespread availablity of imaging devices in low-cost consumer products like mobile phones. To evaluate the performance of recent algorithms in detecting and recognizing text from complex images, the ICDAR 2011 Robust Reading Competition was organized. Challenge 2 of the competition dealt specifically with detecting/recognizing text in natural scene images. This paper presents an overview of the approaches that the participants used, the evaluation measure, and the dataset used in the Challenge 2 of the contest. We also report the performance of all participating methods for text localization and word recognition tasks and compare their results using standard methods of area precision/recall and edit distance.

https://www.dfki.de/web/forschung/publikationen/renameFileForDownload?filename=Shahab-Robust-Reading-ICDAR11.pdf&file_id=uploads_1180

ICDAR 2011 Robust Reading Competition Challenge 2: Reading Text in Scene Images

Locating text in complex color images

An effective algorithm for character recognition in scene images is studied. Scene images are segmented into regions by an image segmentation method based on adaptive thresholding. Character candidate regions are detected by observing gray-level differences between adjacent regions. To ensure extraction of multisegment characters as well as single-segment characters, character pattern candidates are obtained by associating the detected regions according to their positions and gray levels. A character recognition process selects patterns with high similarities by calculating the similarities between character pattern candidates and the standard patterns in a dictionary and then comparing the similarities to the thresholds. A relaxational approach to determine character patterns updates the similarities by evaluating the interactions between categories of patterns, and finally character patterns and their recognition results are obtained. Highly promising experimental results have been obtained using the method on 100 images involving characters of different sizes and formats under uncontrolled lighting. >

Recognizing characters in scene images

An effective extraction algorithm for character recognition in scene images is developed. Character candidate regions are detected by an image segmentation method based on adaptive thresholding and by evaluating gray-level difference between adjacent regions. Character pattern candidates are obtained by associating the detected regions according to their positions and gray levels and evaluating the aspect ratio of the rectangle that circumscribes the associated regions. The character recognition process selects character pattern candidates with high similarities to any categories in a dictionary (high similarity patterns). Highly promising experimental results have been obtained using the method on 100 images acquired under uncontrolled lighting. This algorithm can be applied to read characters of any size and format under various lighting conditions. >

A. Shio

Papers

Recognizing characters in scene images

A relaxational extracting method for character recognition in scene images