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

We propose a method that extracts text regions in natural scene images using low-level image features and that verifies the extracted regions through a high-level text stroke feature. Then the two level features are combined hierarchically. The low-level features are color continuity, gray-level variation and color variance. The color continuity is used since most of the characters in a text region have the same color, and the gray-level variation is used since the text strokes are distinctive to the background in their gray-level values. Also, the color variance is used since the text strokes are distinctive in their colors to the background, and this value is more sensitive than the gray-level variations. As a high level feature, text stroke is examined using multi-resolution wavelet transforms on local image areas and the feature vector is input to a SVM (support vector machine) for verification. We tested the proposed method with various kinds of the natural scene images and confirmed that extraction rates are high even in complex images.

Scene text extraction in natural scene images using hierarchical feature combining and verification

This paper presents a fast license plate localization algorithm for monitoring the highway ticketing systems. The proposed approach can be divided into four sections, that is, vertical edge detection, edge density map generation, binarization and dilation, and license plate location. The algorithm can quickly and correctly detect the region of vehicle license plates. Under the experiment databases, which were taken from real scene, 459 from 478 images are successfully detected. The average accuracy of locating vehicle license plate is 96.00%. The average processing time of one color image is about 100 ms.

http://ieeexplore.ieee.org/iel5/8866/28034/01252633.pdf

A fast license plate extraction method on complex background

Text segmentation in color images using tensor voting

We present a fast automatic text detection algorithm devised for a mobile augmented reality (AR) translation system on a mobile phone. In this application, scene text must be detected, recognized, and translated into a desired language, and then the translation is displayed overlaid properly on the real-world scene. In order to offer a fast automatic text detector, we focused our initial search to find a single letter. Detecting one letter provides useful information that is processed with efficient rules to quickly find the reminder of a word. This approach allows for detecting all the contiguous text regions in an image quickly. We also present a method that exploits the redundancy of the information contained in the video stream to remove false alarms. Our experimental results quantify the accuracy and efficiency of the algorithm and show the strengths and weaknesses of the method as well as its speed (about 160 ms on a recent generation smartphone, not optimized). The algorithm is well suited for real-time, real-world applications.

/pdf/automatic-text-detection-for-mobile-augmented-reality-lvsvt79j79.pdf

Automatic text detection for mobile augmented reality translation

A new connected component based segmentation algorithm which automatically extracts text regions from natural scene images is proposed in this paper. This approach utilizes a multichannel decomposition method to locate text blocks in complex backgrounds. Block alignment analysis and recognition confidence values are used in the combination and identification of the connected components. The algorithm is applied to a test image database and shows promising results.

Automatic text location in natural scene images

This paper focuses on the realization of a bilingual Chinese-English OCR system. First, the Twice-Segment Algorithm is used for segmentation of documents with Chinese and English characters mixed. Then the comprehensive recognition method is employed to improve the robustness of Chinese character recognition. A new measurement of robustness of OCR recognition performance is also put forward here. Finally, exciting experimental results are given.

Youshou Wu

Papers

Automatic text location in natural scene images

Realization of a high-performance bilingual Chinese-English OCR system