Text Localization Algorithm Research Articles

An Efficient Text Recognition System from Complex Color Image for Helping the Visually Impaired Persons

The challenge faced by the visually impaired persons in their day-to-day lives is to interpret text from documents. In this context, to help these people, the objective of this work is to develop an efficient text recognition system that allows the isolation, the extraction, and the recognition of text in the case of documents having a textured background, a degraded aspect of colors, and of poor quality, and to synthesize it into speech. This system basically consists of three algorithms: a text localization and detection algorithm based on mathematical morphology method (MMM); a text extraction algorithm based on the gamma correction method (GCM); and an optical character recognition (OCR) algorithm for text recognition. A detailed complexity study of the different blocks of this text recognition system has been realized. Following this study, an acceleration of the GCM algorithm (AGCM) is proposed. The AGCM algorithm has reduced the complexity in the text recognition system by 70% and kept the same quality of text recognition as that of the original method. To assist visually impaired persons, a graphical interface of the entire text recognition chain has been developed, allowing the capture of images from a camera, rapid and intuitive visualization of the recognized text from this image, and text-to-speech synthesis. Our text recognition system provides an improvement of 6.8% for the recognition rate and 7.6% for the F-measure relative to GCM and AGCM algorithms.

The Smart Assistant for Library Management and Book Reader for Blind People Using Raspberry Pi

This system proposes a camera-based assistive text reading framework to help blind persons ad text labels and product packaging from hand-held objects in their daily lives. To isolate the object from cluttered backgrounds or other surrounding objects in the camera view, we rest propose an ancient and active motion-based method to dine a Region Of Interest (ROI) in the video by asking the user to shake the object. In the extracted ROI, text localization and recognition are conducted to acquire text information. To automatically localize the text regions from the object ROI, we propose a novel text localization algorithm by learning gradient features of stroke orientations and distributions of edge pixels in an Ad boost model. Text characters in the localized text regions are then binaries and recognized by the shelf Optical Character Recognition (OCR) software.

PORTABLE CAMERA-BASED ASSISTIVE TEXT AND PRODUCT LABEL READING FROM HAND-HELD OBJECTS FOR BLIND PERSONS

We propose a camera-based assistive text reading framework to help blind persons read text labels and product packaging from hand-held objects in their daily lives. To isolate the object from cluttered backgrounds or other surrounding objects in the camera view, we first propose an efficient and effective motionbased method to define a region of interest (ROI) in the video by asking the user to shake the object. This method extracts moving object region by a mixture of- Gaussians based background subtraction method. In the extracted ROI, text localization and recognition are conducted to acquire text information. To automatically localize the text regions from the object ROI, we propose a novel text localization algorithm by learning gradient features of stroke orientations and distributions of edge pixels. Text characters in the localized text regions are recognized by off-the-shelf optical character recognition (OCR) software. The recognized text codes are output to blind users in speech. The proof-of-concept prototype is also evaluated on a dataset collected using 10 blind persons, to evaluate the effectiveness of the system's hardware. We explore user interface issues, and assess robustness of the algorithm in extracting and reading text nfrom different objects with complex backgrounds.

ATLAS: Adaptive Text Localization Algorithm in High Color Similarity Background

One of the major problems that occur in text localization process is the issue of color similarity between text and background image. The limitation of localization algorithms due to high color similarity is highlighted in several research papers. Hence, this research focuses towards the improvement of text localizing capability in high color background image similarity by introducing an adaptive text localization algorithm (ATLAS). ATLAS is an edge-based text localization algorithm that consists of two parts. Text-Background Similarity Index (TBSI) being the first part of ATLAS, measures the similarity index of every text region while the second, Multi Adaptive Threshold (MAT), performs multiple adaptive thresholds calculation using size filtration and degree deviation for locating the possible text region. In this research, ATLAS is verified and compared with other localization techniques based on two parameters, localizing strength and precision. The experiment has been implemented and verified using two types of datasets, generated text color spectrum dataset and Document Analysis and Recognition dataset (ICDAR). The result shows ATLAS has significant improvement on localizing strength and slight improvement on precision compared with other localization algorithms in high color text-background image.

Simulated prosthetic vision: improving text accessibility with retinal prostheses.

Image processing can improve significantly the every-day life of blind people wearing current and upcoming retinal prostheses relying on an external camera. We propose to use a real-time text localization algorithm to improve text accessibility. An augmented text-specific rendering based on automatic text localization has been developed. It has been evaluated in comparison to the classical rendering through a Simulated Prosthetic Vision (SPV) experiment with 16 subjects. Subjects were able to detect text in natural scenes much faster and further with the augmented rendering compared to the control rendering. Our results show that current and next generation of low resolution retinal prostheses may benefit from real-time text detection algorithms.

Portable Camera-Based Assistive Text and Product Label Reading From Hand-Held Objects for Blind Persons

We propose a camera-based assistive text reading framework to help blind persons read text labels and product packaging from hand-held objects in their daily lives. To isolate the object from cluttered backgrounds or other surrounding objects in the camera view, we first propose an efficient and effective motion-based method to define a region of interest (ROI) in the video by asking the user to shake the object. This method extracts moving object region by a mixture-of-Gaussians-based background subtraction method. In the extracted ROI, text localization and recognition are conducted to acquire text information. To automatically localize the text regions from the object ROI, we propose a novel text localization algorithm by learning gradient features of stroke orientations and distributions of edge pixels in an Adaboost model. Text characters in the localized text regions are then binarized and recognized by off-the-shelf optical character recognition software. The recognized text codes are output to blind users in speech. Performance of the proposed text localization algorithm is quantitatively evaluated on ICDAR-2003 and ICDAR-2011 Robust Reading Datasets. Experimental results demonstrate that our algorithm achieves the state of the arts. The proof-of-concept prototype is also evaluated on a dataset collected using ten blind persons to evaluate the effectiveness of the system's hardware. We explore user interface issues and assess robustness of the algorithm in extracting and reading text from different objects with complex backgrounds.

Simple and Efficient Text Localization for Compressed Image in Mobile Phone

Extraction of the text data present in images involves Text detection, Text localization, Text tracking, Text extraction, Text Enhancement and Text Recognition. Due to its inherent complexity, traditional text localization algorithms in natural scenes, especially in multi-context scenes, are not implementable under low computational resources architectures such as mobile phones. In this paper, we proposed a simple method to automatically localize signboard texts within JPEG mobile phone camera images. Taking into account the information provided by the Discrete Cosine Transform (DCT) used by the JPEG compression format, we delimitate the borders of the most important text region. This system is simple, reliable, affordable, easily implementable, and quick even working under architectures with low computational resources.

A Two-Level Video Text Localization Algorithm Based on Strokes and Adaboost

This paper proposes a new video text localization algorithm in a localization-to-veriflcation framework. In the localization module, to take full advantage of character stroke attribute, the algorithm introduces a stroke operator which has a strong response to text regions; subsequently, it performs strokes extraction, stroke density flltration and region decomposition to obtain candidate text boxes. In veriflcation module, the algorithm extracts edge oriental histogram features, which have strong discriminabilities for text and non-text, then the Adaboost classifler is used to verify candidate text boxes. Experimental results demonstrate that the proposed algorithm has strong robustness and is capable of obtaining relatively good localization results in various types of video frames.

Text localization and character segmentation algorithms for automatic recognition of slab identification numbers

This paper describes application-oriented text localization and character segmentation algorithms in images. The target text in our application includes many unclear characters due to poor environment as well as the fact that their positions are variable in the images. Consequently, it is difficult to expect a high success rate when using existing text localization algorithms that have been developed for generic texts. Therefore, it is necessary to develop a new text localization algorithm. We propose (1) a coarse algorithm for detecting top and bottom boundaries, (2) a fitness function that is used to decide the true text among the text candidates, (3) two kinds of presegmentation algorithms for calculating the fitness function, and (4) a blank-detecting algorithm that determines whether the text is upside down or not. By the proposed algorithms, input upside-down text is rotated automatically without using any supervised or unsupervised learning methods; further, character segmentation can be done in the process of selecting the true text. To evaluate the algorithms, image data captured by the installed recognition system at Pohang Steel Company (POSCO) are used, and experimental results show that the proposed algorithms are fast and reliable.