Imagery texts are usually organized as a hierarchy of several visual elements, i.e. characters, words, text lines and text blocks. Among these elements, character is the most basic one for various languages such as Western, Chinese, Japanese, mathematical expression and etc. It is natural and convenient to construct a common text detection engine based on character detectors. However, training character detectors requires a vast of location annotated characters, which are expensive to obtain. Actually, the existing real text datasets are mostly annotated in word or line level. To remedy this dilemma, we propose a weakly supervised framework that can utilize word annotations, either in tight quadrangles or the more loose bounding boxes, for character detector training. When applied in scene text detection, we are thus able to train a robust character detector by exploiting word annotations in the rich large-scale real scene text datasets, e.g. ICDAR15 and COCO-text. The character detector acts as a key role in the pipeline of our text detection engine. It achieves the state-of-the-art performance on several challenging scene text detection benchmarks. We also demonstrate the flexibility of our pipeline by various scenarios, including deformed text detection and math expression recognition.
Text line based methods directly estimate line models. These methods are widely adopted in the field of document analysis @cite_24 , where article layout provides strong priors. They are hard to be applied for non-document scenarios.
@cite_24
2058063932 12423284 2252083640 2150731624
In this paper we present an approach for text line analysis and detection in handwritten documents based on Hidden Markov Models, a technique widely used in other handwritten and speech recognition tasks. It is shown that text line analysis and detection can be solved using a more formal methodology in contraposition to most of the proposed heuristic approaches found in the literature. Our approach not only provides the best position coordinates for each of the vertical page regions but also labels them, in this manner surpassing the traditional heuristic methods. In our experiments we demonstrate the performance of the approach (both in line analysis and detection) and study the impact of increasingly constrained "vertical layout language models" on text line detection accuracy. Through this experimentation we also show the improvement in quality of the baselines yielded by our approach in comparison with a state-of-the-art heuristic method based on vertical projection profiles. Document layout analysis is an important task needed for handwritten text recognition among other applications. Text layout commonly found in handwritten legacy documents is in the form of one or more paragraphs composed of parallel text lines. An approach for handwritten text line detection is presented which uses machine-learning techniques and methods widely used in natural language processing. It is shown that text line detection can be accurately solved using a formal methodology, as opposed to most of the proposed heuristic approaches found in the literature. Experimental results show the impact of using increasingly constrained "vertical layout language models" in text line detection accuracy. Text regression has traditionally been tackled using linear models. Here we present a non-linear method based on a deep convolutional neural network. We show that despite having millions of parameters, this model can be trained on only a thousand documents, resulting in a 40 relative improvement over sparse linear models, the previous state of the art. Further, this method is flexible allowing for easy incorporation of side information such as document meta-data. Finally we present a novel technique for interpreting the effect of different text inputs on this complex non-linear model. Topic models provide a powerful tool for analyzing large text collections by representing high dimensional data in a low dimensional subspace. Fitting a topic model given a set of training documents requires approximate inference techniques that are computationally expensive. With today's large-scale, constantly expanding document collections, it is useful to be able to infer topic distributions for new documents without retraining the model. In this paper, we empirically evaluate the performance of several methods for topic inference in previously unseen documents, including methods based on Gibbs sampling, variational inference, and a new method inspired by text classification. The classification-based inference method produces results similar to iterative inference methods, but requires only a single matrix multiplication. In addition to these inference methods, we present SparseLDA, an algorithm and data structure for evaluating Gibbs sampling distributions. Empirical results indicate that SparseLDA can be approximately 20 times faster than traditional LDA and provide twice the speedup of previously published fast sampling methods, while also using substantially less memory.