Text instance as one category of self-described objects provides valuable information for understanding and describing cluttered scenes. The rich and precise high-level semantics embodied in the text could drastically benefit the understanding of the world around us. While most recent visual phrase grounding approaches focus on general objects, this paper explores extracting designated texts and predicting unambiguous scene text information, i.e., to accurately localize and recognize a specific targeted text instance in a cluttered image from natural language descriptions (referring expressions). To address this issue, first a novel recurrent dense text localization network (DTLN) is proposed to sequentially decode the intermediate convolutional representations of a cluttered scene image into a set of distinct text instance detections. Our approach avoids repeated text detections at multiple scales by recurrently memorizing previous detections, and effectively tackles crowded text instances in close proximity. Second, we propose a context reasoning text retrieval (CRTR) model, which jointly encodes text instances and their context information through a recurrent network, and ranks localized text bounding boxes by a scoring function of context compatibility. Third, a recurrent text recognition module is introduced to extend the applicability of aforementioned DTLN and CRTR models, via text verification or transcription. Quantitative evaluations on standard scene text extraction benchmarks and a newly collected scene text retrieval dataset demonstrate the effectiveness and advantages of our models for the joint scene text localization, retrieval, and recognition task.

Text instance provides valuable information for the understanding and interpretation of natural scenes. The rich, precise high-level semantics embodied in the text could be beneficial for understanding the world around us, and empower a wide range of real-world applications. While most recent visual phrase grounding approaches focus on general objects, this paper explores extracting designated texts and predicting unambiguous scene text segmentation mask, i.e. scene text segmentation from natural language descriptions (referring expressions) like orange text on a little boy in black swinging a bat. The solution of this novel problem enables accurate segmentation of scene text instances from the complex background. In our proposed framework, a unified deep network jointly models visual and linguistic information by encoding both region-level and pixel-level visual features of natural scene images into spatial feature maps, and then decode them into saliency response map of text instances. To conduct quantitative evaluations, we establish a new scene text referring expression segmentation dataset: COCO-CharRef. Experimental results demonstrate the effectiveness of the proposed framework on the text instance segmentation task. By combining image-based visual features with language-based textual explanations, our framework outperforms baselines that are derived from state-of-the-art text localization and natural language object retrieval methods on COCO-CharRef dataset.

Text instance as one category of self-described objects provides valuable information for understanding and describing cluttered scenes. In this paper, we explore the task of unambiguous text localization and retrieval, to accurately localize a specific targeted text instance in a cluttered image given a natural language description that refers to it. To address this issue, first a novel recurrent Dense Text Localization Network (DTLN) is proposed to sequentially decode the intermediate convolutional representations of a cluttered scene image into a set of distinct text instance detections. Our approach avoids repeated detections at multiple scales of the same text instance by recurrently memorizing previous detections, and effectively tackles crowded text instances in close proximity. Second, we propose a Context Reasoning Text Retrieval (CRTR) model, which jointly encodes text instances and their context information through a recurrent network, and ranks localized text bounding boxes by a scoring function of context compatibility. Quantitative evaluations on standard scene text localization benchmarks and a newly collected scene text retrieval dataset demonstrate the effectiveness and advantages of our models for both scene text localization and retrieval.

This paper proposes a novel assistive navigation system based on simultaneous localization and mapping (SLAM) and semantic path planning to help visually impaired users navigate in indoor environments. The system integrates multiple wearable sensors and feedback devices including a RGB-D sensor and an inertial measurement unit (IMU) on the waist, a head mounted camera, a microphone and an earplug/speaker. We develop a visual odometry algorithm based on RGB-D data to estimate the user's position and orientation, and refine the orientation error using the IMU. We employ the head mounted camera to recognize the door numbers and the RGB-D sensor to detect major landmarks such as corridor corners. By matching the detected landmarks against the corresponding features on the digitalized floor map, the system localizes the user, and provides verbal instruction to guide the user to the desired destination. The software modules of our system are implemented in Robotics Operating System (ROS). The prototype of the proposed assistive navigation system is evaluated by blindfolded sight persons. The field tests confirm the feasibility of the proposed algorithms and the system prototype.

Text signage as visual indicators in natural scene plays an important role in navigation and notification in our daily life. Most previous methods of scene text extraction are developed from a single scene image. In this paper, we propose a multi-frame based scene text recognition method by tracking text regions in a video captured by a moving camera. The main contributions of this paper are as follows. First, we present a framework of scene text recognition in multiple frames based on feature representation of scene text character (STC) for character prediction and conditional random field (CRF) model for word configuration. Second, a feature representation of STC is employed from dense sampled SIFT descriptors and Fisher Vector. Third, we collect a dataset for text information extraction from natural scene videos. Our proposed multi-frame scene text recognition is more compatible with image/video-based mobile applications. The experimental results demonstrate that STC prediction and word configuration in multiple frames based on text tracking significantly improves the performance of scene text recognition.

Text characters and strings in natural scene can provide valuable information for many applications. Extracting text directly from natural scene images or videos is a challenging task because of diverse text patterns and variant background interferences. This paper proposes a method of scene text recognition from detected text regions. In text detection, our previously proposed algorithms are applied to obtain text regions from scene image. First, we design a discriminative character descriptor by combining several state-of-the-art feature detectors and descriptors. Second, we model character structure at each character class by designing stroke configuration maps. Our algorithm design is compatible with the application of scene text extraction in smart mobile devices. An Android-based demo system is developed to show the effectiveness of our proposed method on scene text information extraction from nearby objects. The demo system also provides us some insight into algorithm design and performance improvement of scene text extraction. The evaluation results on benchmark data sets demonstrate that our proposed scheme of text recognition is comparable with the best existing methods.

Recent technology developments in computer vision, digital cameras, and portable computers make it possible to assist blind individuals by developing camera-based object recognition products. However, motion blur caused by a moving camera limits the real-world application of wayfinding for blind users. In this paper, we propose a new method to detect good quality frames from videos captured by cameras, which are taken by blind users. In our proposed method, both gradient and intensity statistics are extracted from video frames. Then a support vector machine (SVM) based classifier is applied to identify the frames with good quality (Unblurred) from those blurred frames. The Unblurred frames will be further processed to extract essential information for blind wayfinding and navigation such as signage recognition and text extraction. Experimental results demonstrate that our proposed method is able to robustly handle video motions in both indoor and outdoor environments.

Recognizing text character from natural scene images is a challenging problem due to background interferences and multiple character patterns. Scene Text Character (STC) recognition, which generally includes feature representation to model character structure and multi-class classification to predict label and score of character class, mostly plays a significant role in word-level text recognition. The contribution of this paper is a complete performance evaluation of image-based STC recognition, by comparing different sampling methods, feature descriptors, dictionary sizes, coding and pooling schemes, and SVM kernels. We systematically analyze the impact of each option in the feature representation and classification. The evaluation results on two datasets CHARS74K and ICDAR2003 demonstrate that Histogram of Oriented Gradient (HOG) descriptor, soft-assignment coding, max pooling, and Chi-Square Support Vector Machines (SVM) obtain the best performance among local sampling based feature representations. To improve STC recognition, we apply global sampling feature representation. We generate Global HOG (GHOG) by computing HOG descriptor from global sampling. GHOG enables better character structure modeling and obtains better performance than local sampling based feature representations. The GHOG also outperforms existing methods in the two benchmark datasets.

We propose a camera-based assistive framework to help blind persons to read text labels from cylinder objects in their daily life. First, the object is detected from the background or other surrounding objects in the camera view by shaking the object. Then we propose a mosaic model to unwarp the text label on the cylinder object surface and reconstruct the whole label for recognizing text information. This model can handle cylinder objects in any orientations and scales. The text information is then extracted from the unwarped and flatted labels. The recognized text codes are then output to blind users in speech. Experimental results demonstrate the efficiency and effectiveness of the proposed framework from different cylinder objects with complex backgrounds.

The aim of this paper is to design an inexpensive conceivable wearable navigation system that can aid in the navigation of a visually impaired user. A novel approach of utilizing the floor plan map posted on the buildings is used to acquire a semantic plan. The extracted landmarks such as room numbers, doors, etc act as a parameter to infer the way points to each room. This provides a mental mapping of the environment to design a navigation framework for future use. A human motion model is used to predict a path based on how real humans ambulate towards a goal by avoiding obstacles. We demonstrate the possibilities of augmented reality (AR) as a blind user interface to perceive the physical constraints of the real world using haptic and voice augmentation. The haptic belt vibrates to direct the user towards the travel destination based on the metric localization at each step. Moreover, travel route is presented using voice guidance, which is achieved by accurate estimation of the user's location and confirmed by extracting the landmarks, based on landmark localization. The results show that it is feasible to assist a blind user to travel independently by providing the constraints required for safe navigation with user oriented augmented reality.

In this paper, we propose a novel framework to extract text regions from scene images with complex backgrounds and multiple text appearances. This framework consists of three main steps: boundary clustering (BC), stroke segmentation, and string fragment classification. In BC, we propose a new bigram-color-uniformity-based method to model both text and attachment surface, and cluster edge pixels based on color pairs and spatial positions into boundary layers. Then, stroke segmentation is performed at each boundary layer by color assignment to extract character candidates. We propose two algorithms to combine the structural analysis of text stroke with color assignment and filter out background interferences. Further, we design a robust string fragment classification based on Gabor-based text features. The features are obtained from feature maps of gradient, stroke distribution, and stroke width. The proposed framework of text localization is evaluated on scene images, born-digital images, broadcast video images, and images of handheld objects captured by blind persons. Experimental results on respective datasets demonstrate that the framework outperforms state-of-the-art localization algorithms.

In this paper, we propose a novel algorithm, based on stroke components and descriptive Gabor filters, to detect text regions in natural scene images. Text characters and strings are constructed by stroke components as basic units. Gabor filters are used to describe and analyze the stroke components in text characters or strings. We define a suitability measurement to analyze the confidence of Gabor filters in describing stroke component and the suitability of Gabor filters on an image window. From the training set, we compute a set of Gabor filters that can describe principle stroke components of text by their parameters. Then a K-means algorithm is applied to cluster the descriptive Gabor filters. The clustering centers are defined as Stroke Gabor Words (SGWs) to provide a universal description of stroke components. By suitability evaluation on positive and negative training samples respectively, each SGW generates a pair of characteristic distributions of suitability measurements. On a testing natural scene image, heuristic layout analysis is applied first to extract candidate image windows. Then we compute the principle SGWs for each image window to describe its principle stroke components. Characteristic distributions generated by principle SGWs are used to classify text or non-text windows. Experimental results on benchmark datasets demonstrate that our algorithm can handle complex backgrounds and variant text patterns (font, color, scale, etc.).

Text information in natural scene images serves as important clues for many image-based applications such as scene understanding, content-based image retrieval, assistive navigation, and automatic geocoding. However, locating text from a complex background with multiple colors is a challenging task. In this paper, we explore a new framework to detect text strings with arbitrary orientations in complex natural scene images. Our proposed framework of text string detection consists of two steps: 1) image partition to find text character candidates based on local gradient features and color uniformity of character components and 2) character candidate grouping to detect text strings based on joint structural features of text characters in each text string such as character size differences, distances between neighboring characters, and character alignment. By assuming that a text string has at least three characters, we propose two algorithms of text string detection: 1) adjacent character grouping method and 2) text line grouping method. The adjacent character grouping method calculates the sibling groups of each character candidate as string segments and then merges the intersecting sibling groups into text string. The text line grouping method performs Hough transform to fit text line among the centroids of text candidates. Each fitted text line describes the orientation of a potential text string. The detected text string is presented by a rectangle region covering all characters whose centroids are cascaded in its text line. To improve efficiency and accuracy, our algorithms are carried out in multi-scales. The proposed methods outperform the state-of-the-art results on the public Robust Reading Dataset, which contains text only in horizontal orientation. Furthermore, the effectiveness of our methods to detect text strings with arbitrary orientations is evaluated on the Oriented Scene Text Dataset collected by ourselves containing text strings in nonhorizontal orientations.

Many reading assistants and navigation systems have been designed specifically for people who are blind or visually impaired, but text locating in scene image with complex background has not yet been successfully addressed. In this paper, we propose a novel method to locate scene text by combining color uniformity and high edge density together. We perform structural analysis of text strings which contain several characters in alignment. First, we calculate the edge image and then repaint the corresponding edge pixels in the original image by using a non-dominant color. Second, color reduction is performed by color histogram and K-means algorithms to segment the repainted image into color layers. Third, we perform edge detection and label the boundaries of both text characters and unexpected noises in each color layer. Each centroid is assigned a degree which is the number of overlap in the same position among color layers. Fourth, text line fitting among centroids with high degree is performed to cascade the character boundaries which belong to the same text string. The detected text string is presented by a rectangle region covering all character boundaries in its text line. Experimental results demonstrate that our algorithm is able to locate text strings with arbitrary orientations. The performance of our algorithm is comparable with the state-of-art algorithms.

Independent travel is a well known challenge for blind or visually impaired persons. In this paper, we propose a computer vision-based indoor wayfinding system for assisting blind people to independently access unfamiliar buildings. In order to find different rooms (i.e. an office, a lab, or a bathroom) and other building amenities (i.e. an exit or an elevator), we incorporate door detection with text recognition. First we develop a robust and efficient algorithm to detect doors and elevators based on general geometric shape, by combining edges and corners. The algorithm is generic enough to handle large intra-class variations of the object model among different indoor environments, as well as small inter-class differences between different objects such as doors and elevators. Next, to distinguish an office door from a bathroom door, we extract and recognize the text information associated with the detected objects. We first extract text regions from indoor signs with multiple colors. Then text character localization and layout analysis of text strings are applied to filter out background interference. The extracted text is recognized by using off-the-shelf optical character recognition (OCR) software products. The object type, orientation, and location can be displayed as speech for blind travelers.