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This paper proposes an end-to-end framework, namely fully convolutional recurrent network (FCRN) for handwritten Chinese text recognition (HCTR). Unlike traditional methods that rely heavily on segmentation, our FCRN is trained with online text data directly and learns to associate the pen-tip trajectory with a sequence of characters. FCRN consists of four parts: a path-signature layer to extract...
Scene text detection and recognition have become active research topics in computer vision. In this paper, we focus on the detection of text proposal from wild images. Text proposals attempt to generate a relatively small set of bounding box proposals that are most likely to contain text. Different from previous methods that merge similar region based on property of individual region, we assumed that...
With the rapid increase of multimedia data, textual content in an image has become a very important source of information for several applications like navigation, image search and retrieval, image understanding, captioning, machine translation and several others. Scene text localization is the first step towards such applications and most current methods focus on generating a small set of high precision...
Scene text is one of the most important information sources for our daily life because it has particular functions such as disambiguation and navigation. In contrast, ordinary document text has no such function. Consequently, it is natural to have a hypothesis that scene text and document text have different characteristics. This paper tries to prove this hypothesis by semantic analysis of texts by...
In printed stylized documents, text lines may be curved in shape and as a result characters of a single line may be multi-oriented. This paper presents a multi-scale and multi-oriented character recognition scheme using foreground as well as background information. Here each character is partitioned into multiple circular zones. For each zone, three centroids are computed by grouping the constituent...
This paper proposes a novel segmentation-free approach using deep neural network based hidden Markov model (DNN-HMM) for offline handwritten Chinese text recognition. In the general Bayesian framework, three key issues are comprehensively investigated, namely feature extraction, character modeling, and language modeling. First, as for the feature extraction on the basis of each frame or sliding window,...
In recent years, growing attention has been paid to recognizing text in natural scenes images. Scene Character recognition (SCR) is an important step in automatizing the process of reading text in natural scenes.
Recognizing text in historical maps is inherently difficult due to input challenges such as artifacts interfering with the text or an unpredictable rotation and orientation of the text. This paper discusses our algorithm that overcomes the limitations of the input by adding extra input consisting of multiple layers of images of the same map area but across different time periods and names of geographic...
This paper describes the AcTiVComp: detection and recognition of Arabic Text in Video competition in conjunction with the 23rd International Conference on Pattern Recognition (ICPR). The main objective of this competition is to evaluate the performance of participants' algorithms to automatically locate and/or recognize overlay text lines in Arabic video frames using the freely available AcTiV dataset...
Developing a unified text detection and recognition method is hard for different video types due to varying characteristics in video. This paper proposes a new method for categorizing different types of video text frames, namely, videos containing advertisement, signboard, license plate, front page of book or magazine, street view, and video of general items, for better text detection and recognition...
Blur is a common artifact in video, which adds more complexity to text detection and recognition. To achieve good accuracies for text detection and recognition, this paper suggests a new method for classifying blurred and non-blurred frames in video. We explore quality metrics, namely, BRISQUE, NRIQA, GPC and SI, in a new way for classification. We estimate the values of these metrics with the help...
The authors have conducted studies on recognizing Arabic news captions to develop a system for video retrieval to index and edit Arabic broadcast programs daily received and stored in big database. This paper describes a dedicated OCR for recognizing low resolution news captions in video images. News caption recognition system consisting of text line extraction, word segmentation and segmentation-recognition...
Natural scene text recognition has proved to be challenging due to the unconstrained wild conditions. In this paper, to solve this problem we propose a method which first detects and recognizes characters by utilizing the high performance Convolutional Neural Network (CNN). Then for post-processing, inspired by its success in speech recognition, we employ the efficient and flexible Weight Finite State...
Scene text information extraction plays an important role in many computer vision applications. Unlike most existing text extraction algorithms for English texts, in this paper, we focus on Chinese texts, which are more complex in stroke and structure. To tackle this challenging problem, we propose a novel convolutional neural network (CNN) based text structure feature extractor for Chinese texts...
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