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This paper considers using deep neural networks for handwritten Chinese character recognition (HCCR) with arbitrary position, scale, and orientations. To solve this problem, we combine the recently proposed spatial transformer network (STN) with the deep residual network (DRN). The STN acts like a character shape normalization procedure. Different from the traditional heuristic shape normalization...
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...
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...
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...
Perturbation-based recognition is effective to recover the deformation of handwritten characters and improve the recognition performance by generating multiple distortions and selecting a distortion that best restores character deformation. Considering that the characters in a field undergo similar deformation under a consistent style, we proposed style consistent perturbation for handwritten character...
This paper presents an effective approach for the offline recognition of unconstrained handwritten Chinese texts. Under the general integrated segmentation-and-recognition framework with character oversegmentation, we investigate three important issues: candidate path evaluation, path search, and parameter estimation. For path evaluation, we combine multiple contexts (character recognition scores,...
In offline handwritten text recognition, the separation of touching characters remains a challenge due to the variability of touching structures. This paper proposes a new touching character separation method for Chinese handwriting based on skeleton analysis and contour analysis incorporating the visibility of separating points. Separating points are detected from strokes that are common in both...
This paper introduces a pair of online and offline Chinese handwriting databases, containing samples of isolated characters and handwritten texts. The samples were produced by 1,020 writers using Anoto pen on papers for obtaining both online trajectory data and offline images. Both the online samples and offline samples are divided into six datasets, three for isolated characters (DB1.0-C1.2) and...
This paper presents a conditional random field (CRF) model for aligning online handwritten Chinese/Japanese text lines (character strings) with the corresponding transcripts. The CRF model is defined on a lattice which contains all possible segmentation hypotheses. The feature functions characterize the shape and context dependences of characters, including the scores of character recognition and...
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...
This paper investigates the effects of confidence transformation (CT) of the character classifier outputs in handwritten Chinese text recognition. The classifier outputs are transformed to confidence values in three confidence types, namely, sigmoid, soft max and Dempster-Shafer theory of evidence (D-S evidence). The confidence parameters are optimized by minimizing the cross-entropy (CE) loss function...
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...
The splitting of touching characters remains a challenge in over-segmentation, which is crucial to the performance of integrated segmentation-recognition of handwritten character strings. In this paper, we propose a new method based on contour analysis for touching character splitting in Chinese handwriting. To reliably locate splitting points on the contour of touching pattern, we pair upper and...
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...
Annotating the regions, text lines and characters of document images is an important, but tedious and expensive task. A ground-truthing tool may largely alleviate the human burden in this process. This paper describes an automated recognition-based tool GTLC for finding the best alignment between the text transcript and the connected components of unconstrained handwritten document image. The alignment...
This paper describes a system for handwritten Chinese text recognition integrating language model. On a text line image, the system generates character segmentation and word segmentation candidates, and the candidate paths are evaluated by character recognition scores and language model. The optimal path, giving segmentation and recognition result, is found using a pruned dynamic programming search...
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