CFOR: Character-First Open-Set Text Recognition via Context-Free Learning.

The open-set text recognition task is a generalized form of the (close-set) text recognition task, where the model is further challenged to spot and incrementally recognize novel characters in new languages, which also indicate a shift in the language model. In this work, we propose to alleviate the confounding effect such biases under an open-set setup by learning individual character representations out of their context.We propose a Character-First Open-Set Text Recognition framework that treats individual characters as first-class citizens by cotraining the feature extractor with two context-free learning tasks.

Inverse-Like Antagonistic Scene Text Spotting via Reading-Order Estimation and Dynamic Sampling.

Scene text spotting is a challenging task, especially for inverse-like scene text, which has complex layouts, e.g., mirrored, symmetrical, or retro-flexed.

Arbitrary Shape Text Detection via Segmentation With Probability Maps.

Arbitrary shape text detection is a challenging task due to the significantly varied sizes and aspect ratios, arbitrary orientations or shapes, inaccurate annotations, etc. Due to the scalability of pixel-level prediction, segmentation-based methods can adapt to various shape texts and hence attracted considerable attention recently. However, accurate pixel-level annotations of texts are formidable, and the existing datasets for scene text detection only provide coarse-grained boundary annotations.

Kernel Proposal Network for Arbitrary Shape Text Detection.

Segmentation-based methods have achieved great success for arbitrary shape text detection. However, separating neighboring text instances is still one of the most challenging problems due to the complexity of texts in scene images. In this article, we propose an innovative kernel proposal network (dubbed KPN) for arbitrary shape text detection.

Health assistant: answering your questions anytime from biomedical literature.

Motivation: With the abundant medical resources, especially literature available online, it is possible for people to understand their own health status and relevant problems autonomously. However, how to obtain the most appropriate answer from the increasingly large-scale database, remains a great challenge. Here, we present a biomedical question answering framework and implement a system, Health Assistant, to enable the search process.

Detecting Multi-Resolution Pedestrians Using Group Cost-Sensitive Boosting with Channel Features.

Significant progress has been achieved in the past few years for the challenging task of pedestrian detection. Nevertheless, a major bottleneck of existing state-of-the-art approaches lies in a great drop in performance with reducing resolutions of the detected targets. For the boosting-based detectors which are popular in pedestrian detection literature, a possible cause for this drop is that in their boosting training process, low-resolution samples, which are usually more difficult to be detected due to the missing details, are still treated equally importantly as high-resolution samples, resulting in the false negatives since they are more easily rejected in the early stages and can hardly be recovered in the late stages.

Biomedical literature classification with a CNNs-based hybrid learning network.

Deep learning techniques, e.g., Convolutional Neural Networks (CNNs), have been explosively applied to the research in the fields of information retrieval and natural language processing.

Tracking Based Multi-Orientation Scene Text Detection: A Unified Framework With Dynamic Programming.

There are a variety of grand challenges for multi-orientation text detection in scene videos, where the typical issues include skew distortion, low contrast, and arbitrary motion. Most conventional video text detection methods using individual frames have limited performance. In this paper, we propose a novel tracking based multi-orientation scene text detection method using multiple frames within a unified framework via dynamic programming.

A Unified Framework for Tracking Based Text Detection and Recognition from Web Videos.

Video text extraction plays an important role for multimedia understanding and retrieval. Most previous research efforts are conducted within individual frames. A few of recent methods, which pay attention to text tracking using multiple frames, however, do not effectively mine the relations among text detection, tracking and recognition.

Text Detection, Tracking and Recognition in Video: A Comprehensive Survey.

The intelligent analysis of video data is currently in wide demand because a video is a major source of sensory data in our lives. Text is a prominent and direct source of information in video, while the recent surveys of text detection and recognition in imagery focus mainly on text extraction from scene images. Here, this paper presents a comprehensive survey of text detection, tracking, and recognition in video with three major contributions.

ISART: A Generic Framework for Searching Books with Social Information.

Effective book search has been discussed for decades and is still future-proof in areas as diverse as computer science, informatics, e-commerce and even culture and arts. A variety of social information contents (e.g, ratings, tags and reviews) emerge with the huge number of books on the Web, but how they are utilized for searching and finding books is seldom investigated.

A VidEo-Based Intelligent Recognition and Decision System for the Phacoemulsification Cataract Surgery.

The phacoemulsification surgery is one of the most advanced surgeries to treat cataract. However, the conventional surgeries are always with low automatic level of operation and over reliance on the ability of surgeons. Alternatively, one imaginative scene is to use video processing and pattern recognition technologies to automatically detect the cataract grade and intelligently control the release of the ultrasonic energy while operating.

Multi-Orientation Scene Text Detection with Adaptive Clustering.

Text detection in natural scene images is an important prerequisite for many content-based image analysis tasks, while most current research efforts only focus on horizontal or near horizontal scene text. In this paper, first we present a unified distance metric learning framework for adaptive hierarchical clustering, which can simultaneously learn similarity weights (to adaptively combine different feature similarities) and the clustering threshold (to automatically determine the number of clusters). Then, we propose an effective multi-orientation scene text detection system, which constructs text candidates by grouping characters based on this adaptive clustering.

DeTEXT: A Database for Evaluating Text Extraction from Biomedical Literature Figures.

Hundreds of millions of figures are available in biomedical literature, representing important biomedical experimental evidence. Since text is a rich source of information in figures, automatically extracting such text may assist in the task of mining figure information. A high-quality ground truth standard can greatly facilitate the development of an automated system.

Learning document semantic representation with hybrid deep belief network.

High-level abstraction, for example, semantic representation, is vital for document classification and retrieval. However, how to learn document semantic representation is still a topic open for discussion in information retrieval and natural language processing. In this paper, we propose a new Hybrid Deep Belief Network (HDBN) which uses Deep Boltzmann Machine (DBM) on the lower layers together with Deep Belief Network (DBN) on the upper layers.

Robust Text Detection in Natural Scene Images.

Text detection in natural scene images is an important prerequisite for many content-based image analysis tasks. In this paper, we propose an accurate and robust method for detecting texts in natural scene images. A fast and effective pruning algorithm is designed to extract Maximally Stable Extremal Regions (MSERs) as character candidates using the strategy of minimizing regularized variations.