Improving Semantic Segmentation via Efficient Self-Training.

Starting from the seminal work of Fully Convolutional Networks (FCN), there has been significant progress on semantic segmentation. However, deep learning models often require large amounts of pixelwise annotations to train accurate and robust models. Given the prohibitively expensive annotation cost of segmentation masks, we introduce a self-training framework in this paper to leverage pseudo labels generated from unlabeled data. In order to handle the data imbalance problem of semantic segmentation, we propose a centroid sampling strategy to uniformly select training samples from every class within each epoch. We also introduce a fast training schedule to alleviate the computational burden. This enables us to explore the usage of large amounts of pseudo labels. Our Centroid Sampling based Self-Training framework (CSST) achieves state-of-the-art results on Cityscapes and CamVid datasets. On PASCAL VOC 2012 test set, our models trained with the original train set even outperform the same models trained on the much bigger augmented train set. This indicates the effectiveness of CSST when there are fewer annotations. We also demonstrate promising few-shot generalization capability from Cityscapes to BDD100K and from Cityscapes to Mapillary datasets.

Dependence Models for Searching Text in Document Images.

The main goal of existing word spotting approaches for searching document images has been the identification of visually similar word images in the absence of high quality text recognition output. Searching for a piece of arbitrary text is not possible unless the user identifies a sample word image from the document collection or generates the query word image synthetically. To address this problem, a Markov Random Field (MRF) framework is proposed for searching document images and shown to be effective for searching arbitrary text in real time for books printed in English (Latin script), Telugu and Ottoman scripts. The English experiments demonstrate that the dependencies between the visual terms and letter bigrams can be automatically learned using noisy OCR output. It is also shown that OCR text search accuracy can be significantly improved if it is combined with the proposed approach. No commercial OCR engine is available for Telugu or Ottoman script. In these cases the dependencies are trained using manually annotated document images. It is demonstrated that the trained model can be directly used to resolve arbitrary text queries across books despite font type and size differences. The proposed approach outperforms a state-of-the-art BLSTM baseline in these contexts.

A novel word spotting method based on recurrent neural networks.

Keyword spotting refers to the process of retrieving all instances of a given keyword from a document. In the present paper, a novel keyword spotting method for handwritten documents is described. It is derived from a neural network-based system for unconstrained handwriting recognition. As such it performs template-free spotting, i.e., it is not necessary for a keyword to appear in the training set. The keyword spotting is done using a modification of the CTC Token Passing algorithm in conjunction with a recurrent neural network. We demonstrate that the proposed systems outperform not only a classical dynamic time warping-based approach but also a modern keyword spotting system, based on hidden Markov models. Furthermore, we analyze the performance of the underlying neural networks when using them in a recognition task followed by keyword spotting on the produced transcription. We point out the advantages of keyword spotting when compared to classic text line recognition.

A scale space approach for automatically segmenting words from historical handwritten documents.

Many libraries, museums, and other organizations contain large collections of handwritten historical documents, for example, the papers of early presidents like George Washington at the Library of Congress. The first step in providing recognition/ retrieval tools is to automatically segment handwritten pages into words. State of the art segmentation techniques like the gap metrics algorithm have been mostly developed and tested on highly constrained documents like bank checks and postal addresses. There has been little work on full handwritten pages and this work has usually involved testing on clean artificial documents created for the purpose of research. Historical manuscript images, on the other hand, contain a great deal of noise and are much more challenging. Here, a novel scale space algorithm for automatically segmenting handwritten (historical) documents into words is described. First, the page is cleaned to remove margins. This is followed by a gray-level projection profile algorithm for finding lines in images. Each line image is then filtered with an anisotropic Laplacian at several scales. This procedure produces blobs which correspond to portions of characters at small scales and to words at larger scales. Crucial to the algorithm is scale selection, that is, finding the optimum scale at which blobs correspond to words. This is done by finding the maximum over scale of the extent or area of the blobs. This scale maximum is estimated using three different approaches. The blobs recovered at the optimum scale are then bounded with a rectangular box to recover the words. A postprocessing filtering step is performed to eliminate boxes of unusual size which are unlikely to correspond to words. The approach is tested on a number of different data sets and it is shown that, on 100 sampled documents from the George Washington corpus of handwritten document images, a total error rate of 17 percent is observed. The technique outperforms a state-of-the-art gap metrics word-segmentation algorithm on this collection.

Information-sharing in out-of-hospital disaster response: the future role of information technology.

Numerous examples exist of the benefits of the timely access to information in emergencies and disasters. Information technology (IT) is playing an increasingly important role in information-sharing during emergencies and disasters. The effective use of IT in out-of-hospital (OOH) disaster response is accompanied by numerous challenges at the human, applications, communication, and security levels. Most reports of IT applications to emergencies or disasters to date, concern applications that are hospital-based or occur during non-response phases of events (i.e., mitigation, planning and preparedness, or recovery phases). Few reports address the application of IT to OOH disaster response. Wireless peer networks that involve ad hoc wireless routing networks and peer-to-peer application architectures offer a promising solution to the many challenges of information-sharing in OOH disaster response. These networks offer several services that are likely to improve information-sharing in OOH emergency response, including needs and capacity assessment databases, victim tracking, event logging, information retrieval, and overall incident management system support.