Enhancing Energy Minimization Framework for Scene Text Recognition with Top-Down Cues

Recognizing scene text is a challenging problem, even more so than the recognition of scanned documents. This problem has gained significant attention from the computer vision community in recent years, and several methods based on energy minimization frameworks and deep learning approaches have been proposed. In this work, we focus on the energy minimization framework and propose a model that exploits both bottom-up and top-down cues for recognizing cropped words extracted from street images. The bottom-up cues are derived from individual character detections from an image. We build a conditional random field model on these detections to jointly model the strength of the detections and the interactions between them. These interactions are top-down cues obtained from a lexicon-based prior, i.e., language statistics. The optimal word represented by the text image is obtained by minimizing the energy function corresponding to the random field model. We evaluate our proposed algorithm extensively on a number of cropped scene text benchmark datasets, namely Street View Text, ICDAR 2003, 2011 and 2013 datasets, and IIIT 5K-word, and show better performance than comparable methods. We perform a rigorous analysis of all the steps in our approach and analyze the results. We also show that state-of-the-art convolutional neural network features can be integrated in our framework to further improve the recognition performance

Speeding up Convolutional Neural Networks with Low Rank Expansions

The focus of this paper is speeding up the evaluation of convolutional neural networks. While delivering impressive results across a range of computer vision and machine learning tasks, these networks are computationally demanding, limiting their deployability. Convolutional layers generally consume the bulk of the processing time, and so in this work we present two simple schemes for drastically speeding up these layers. This is achieved by exploiting cross-channel or filter redundancy to construct a low rank basis of filters that are rank-1 in the spatial domain. Our methods are architecture agnostic, and can be easily applied to existing CPU and GPU convolutional frameworks for tuneable speedup performance. We demonstrate this with a real world network designed for scene text character recognition, showing a possible 2.5x speedup with no loss in accuracy, and 4.5x speedup with less than 1% drop in accuracy, still achieving state-of-the-art on standard benchmarks

Document Layout Analysis and Recognition Systems

Automatic extraction of relevant knowledge to domain-specific questions from Optical Character Recognition (OCR) documents is critical for developing intelligent systems, such as document search engines, sentiment analysis, and information retrieval, since hands-on knowledge extraction by a domain expert with a large volume of documents is intensive, unscalable, and time-consuming. There have been a number of studies that have automatically extracted relevant knowledge from OCR documents, such as ABBY and Sandford Natural Language Processing (NLP). Despite the progress, there are still limitations yet-to-be solved. For instance, NLP often fails to analyze a large document. In this thesis, we propose a knowledge extraction framework, which takes domain-specific questions as input and provides the most relevant sentence/paragraph to the given questions in the document. Overall, our proposed framework has two phases. First, an OCR document is reconstructed into a semi-structured document (a document with hierarchical structure of (sub)sections and paragraphs). Then, relevant sentence/paragraph for a given question is identified from the reconstructed semi structured document. Specifically, we proposed (1) a method that converts an OCR document into a semi structured document using text attributes such as font size, font height, and boldface (in Chapter 2), (2) an image-based machine learning method that extracts Table of Contents (TOC) to provide an overall structure of the document (in Chapter 3), (3) a document texture-based deep learning method (DoT-Net) that classifies types of blocks such as text, image, and table (in Chapter 4), and (4) a Question & Answer (Q&A) system that retrieves most relevant sentence/paragraph for a domain-specific question. A large number of document intelligent systems can benefit from our proposed automatic knowledge extraction system to construct a Q&A system for OCR documents. Our Q&A system has applied to extract domain specific information from business contracts at GE Power

Cross-codex Learning for Reliable Scribe Identification in Medieval Manuscripts

Historic scribe identification is a substantial task for obtaining information about the past. Uniform script styles, such as the Carolingian minuscule, make it a difficult task for classification to focus on meaningful features. Therefore, we demonstrate in this paper the importance of cross-codex training data for CNN based text-independent off-line scribe identification, to overcome codex dependent overfitting. We report three main findings: First, we found that preprocessing with masked grayscale images instead of RGB images clearly increased the F1-score of the classification results. Second, we trained different neural networks on our complex data, validating time and accuracy differences in order to define the most reliable network architecture. With AlexNet, the network with the best trade-off between F1-score and time, we achieved for individual classes F1-scores of up to 0,96 on line level and up to 1.0 on page level in classification. Third, we could replicate the finding that the CNN output can be further improved by implementing a reject option, giving more stable results. We present the results on our large scale open source dataset -- the Codex Claustroneoburgensis database (CCl-DB) -- containing a significant number of writings from different scribes in several codices. We demonstrate for the first time on a dataset with such a variety of codices that paleographic decisions can be reproduced automatically and precisely with CNNs. This gives manifold new and fast possibilities for paleographers to gain insights into unlabeled material, but also to develop further hypotheses