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In this paper, we first provide a new perspective to divide existing high performance object detection methods into direct and indirect regressions. Direct regression performs boundary regression by predicting the offsets from a given point, while indirect regression predicts the offsets from some bounding box proposals. In the context of multioriented scene text detection, we analyze the drawbacks...
Writer adaptation is an important topic in handwriting recognition, which can further improve the performance of writer-independent recognizer. In this paper, we propose combining the neural network classifier with style transfer mapping (STM) for unsupervised writer adaptation, which only require writer-specific unlabeled data, and therefore is more common and efficient compared to supervised adaptation...
Over-segmentation is often used in text recognition to generate candidate characters. In this paper, we propose a neural network-based over-segmentation method for cropped scene text recognition. On binarized text line image, a segmentation window slides over each connected component, and a neural network is used to classify whether the window locates a segmentation point or not. We evaluate several...
Character string recognition based on over segmentation by integrating character classifier and context models has been demonstrated successful. Geometric context models characterizing the candidate character likeliness and between character relationship have shown benefits in several scripts but have not been evaluated in numeral string recognition. Compared with Chinese scripts mixed with alphanumeric...
Discriminative feature extraction (DFE) is an effective linear dimensionality reduction method for pattern recognition. It improves the recognition performance via optimizing subspace projection axes and classifier parameters simultaneously. In this paper, we propose a nonlinear extension of DFE, called discriminative quadratic feature extraction (DQFE), for which feature vectors are firstly mapped...
This paper describes the Chinese handwriting recognition competition held at the 12th International Conference on Document Analysis and Recognition (ICDAR 2013). This third competition in the series again used the CASIA-HWDB/OLHWDB databases as the training set, and all the submitted systems were evaluated on closed datasets to report character-level correct rates. This year, 10 groups submitted 27...
The discriminative training of classifiers for handwritten Chinese character recognition (HCCR) is highly demanding in computation due to the large number of categories. The inability of discriminative training with large sample set on personal computers has hindered the accuracy promotion for HCCR. To overcome this problem, we have implemented the training algorithm of discriminative learning quadratic...
In this paper, we propose an efficient scene text localization method using gradient local correlation, which can characterize the density of pair wise edges and stroke width consistency to get a text confidence map. Gradient local correlation is insensitive to the gradient direction and robust to noise, small character size and shadow. Based on the text confidence map, the regions with high confidence...
Blogosphere has become a hot research field in recent years. As the existing detection algorithm has problems of inefficient feature selection and weak correlation, we propose an algorithm of splog detection based on features relation tree. We could construct the tree according to the correlation of the features, reserving the strong relevance features and removing the weak ones, then prune the redundant...
This paper proposes a method for keyword spotting in offline Chinese handwritten documents using a statistical model. On a text query word, the method measures the similarity between the query word and every candidate word in the document by combining a character classifier and four classifiers characterizing the geometric contexts. By over-segmenting text lines into primitive segments, candidate...
In the Chinese handwriting recognition competition organized with the ICDAR 2011, four tasks were evaluated: offline and online isolated character recognition, offline and online handwritten text recognition. To enable the training of recognition systems, we announced the large databases CASIA-HWDB/OLHWDB. The submitted systems were evaluated on un-open datasets to report character-level correct rates...
The alignment of text line images with text transcript is a crucial step of handwritten document annotation. Handwritten text alignment is prone to errors due to the difficulty of character segmentation and the variability of character shape, size and position. In this paper, we propose to incorporate the geometric context of character strings to improve the alignment accuracy for offline handwritten...
Chinese handwriting recognition remains a challenge. Research works have reported very high accuracies on neatly handwritten characters yet the performance on unconstrained handwriting remains quite low. To promote the recognition technology, new databases of unconstrained handwriting have been constructed for academic research and benchmarking. This paper reports the contest results of online and...
Text line extraction from unconstrained handwritten documents is a challenge because the text lines are often skewed and curved and the space between lines is not obvious. To solve this problem, we propose an approach based on minimum spanning tree (MST) clustering with new distance measures. First, the connected components of the document image are grouped into a tree by MST clustering with a new...
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