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, geometric and linguistic contexts) from the Bayesian decision view, and convert the classifier outputs to posterior probabilities via confidence transformation. In path search, we use a refined beam search algorithm to improve the search efficiency and, meanwhile, use a candidate character augmentation strategy to improve the recognition accuracy. The combining weights of the path evaluation function are optimized by supervised learning using a Maximum Character Accuracy criterion. We evaluated the recognition performance on a Chinese handwriting database CASIA-HWDB, which contains nearly four million character samples of 7,356 classes and 5,091 pages of unconstrained handwritten texts. The experimental results show that confidence transformation and combining multiple contexts improve the text line recognition performance significantly. On a test set of 1,015 handwritten pages, the proposed approach achieved character-level accurate rate of 90.75 percent and correct rate of 91.39 percent, which are superior by far to the best results reported in the literature.

http://nlpr-web.ia.ac.cn/2012papers/gjkw/gk35.pdf

Handwritten Chinese Text Recognition by Integrating Multiple Contexts

This paper proposes a method for handwritten Chinese/Japanese text (character string) recognition based on semi-Markov conditional random fields (semi-CRFs). The high-order semi-CRF model is defined on a lattice containing all possible segmentation-recognition hypotheses of a string to elegantly fuse the scores of candidate character recognition and the compatibilities of geometric and linguistic contexts by representing them in the feature functions. Based on given models of character recognition and compatibilities, the fusion parameters are optimized by minimizing the negative log-likelihood loss with a margin term on a training string sample set. A forward-backward lattice pruning algorithm is proposed to reduce the computation in training when trigram language models are used, and beam search techniques are investigated to accelerate the decoding speed. We evaluate the performance of the proposed method on unconstrained online handwritten text lines of three databases. On the test sets of databases CASIA-OLHWDB (Chinese) and TUAT Kondate (Japanese), the character level correct rates are 95.20 and 95.44 percent, and the accurate rates are 94.54 and 94.55 percent, respectively. On the test set (online handwritten texts) of ICDAR 2011 Chinese handwriting recognition competition, the proposed method outperforms the best system in competition.

/pdf/handwritten-chinese-japanese-text-recognition-using-semi-3l6hokz1fr.pdf

Handwritten Chinese/Japanese Text Recognition Using Semi-Markov Conditional Random Fields

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. In total, we received 25 systems submitted by eight groups. On the test datasets, the best results (correct rates) are 92.18% for offline character recognition, 95.77% for online character recognition, 77.26% for offline text recognition, and 94.33% for online text recognition, respectively. In addition to the evaluation results, we provide short descriptions of the recognition methods and have brief discussions.

/pdf/icdar-2011-chinese-handwriting-recognition-competition-21lv73v6u9.pdf

ICDAR 2011 Chinese Handwriting Recognition Competition

Offline Chinese handwriting recognition (OCHR) is a typically difficult pattern recognition problem. Many authors have presented various approaches to recognizing its different aspects. We present a survey and an assessment of relevant papers appearing in recent publications of relevant conferences and journals, including those appearing in ICDAR, SDIUT, IWFHR, ICPR, PAMI, PR, PRL, SPIEDRR, and IJDAR. The methods are assessed in the sense that we document their technical approaches, strengths, and weaknesses, as well as the data sets on which they were reportedly tested and on which results were generated. We also identify a list of technology gaps with respect to Chinese handwriting recognition and identify technical approaches that show promise in these areas as well as identify the leading researchers for the applicable topics, discussing difficulties associated with any given approach.

Offline Chinese handwriting recognition: An assessment of current technology

http://www.nlpr.ia.ac.cn/2012papers/gjkw/gk36.pdf

An approach for real-time recognition of online Chinese handwritten sentences

This paper presents a new method for segmenting and recognizing Chinese handwritten address character strings. First, a dissection algorithm is applied to over-segment string image into radical series so that the correct segmentation could be achieved by merging those radicals according to the correct merging path. Then, the method synthesizes layout analysis, isolated character classifier and bi-gram language model to find the best merging path and the best recognition result. The classifier used in this paper will give every isolated character image ten recognition candidates, each with corresponding recognition confidence. The parameter of bi-gram model is obtained from address database which contains more than one hundred thousand address items. In experiments on 946 mail images, the proposed method achieves correct rate of 87.2 percent

A Novel Segmentation and Recognition Algorithm for Chinese Handwritten Address Character Strings

An efficient method of Chinese handwritten address character string segmentation and recognition is presented. First, an address string image is presegmented into several radicals using stroke extraction and stroke mergence. Next, the radical series obtained by presegmentation merge into different character image series according to different merging paths. After that, the optimal merging path is selected using recognition and semantic information. The recognition information is given by the character classifier. The semantic information is obtained from large scale address database containing more than one hundred thousand address items. Finally, the optimal recognition results of the character image series which are combined by radical series according to the optimal merging paths are obtained. In experiments on 897 mail images, the proposed method achieves correct rate of 85 percent while the error rate is 15 percent.

A hidden Markov model based segmentation and recognition algorithm for Chinese handwritten address character strings

This paper presents a context driven segmentation and recognition method for handwritten Chinese characters. We follow a split-merge technique in character segmentation. In this process, a Chinese text line is first pre-segmented into a sequence of radicals, which are then merged according to a cost function combining both recognition confidence and contextual cost. Two strategies are also proposed for implementation: bi-gram based merging and lexicon driven merging. In the former one, we generate a set of merging paths which are then evaluated by Viterbi algorithm. The radicals’ best merging method is given by the path with the highest score. In the latter strategy, a lexicon is preset and compared with the radicals to determine both radicals’ merging and candidate character selection. Experiments show that contextual information plays a crucial role in Chinese character segmentation and could obviously improve the segmentation and recognition results.

/pdf/context-driven-chinese-string-segmentation-and-recognition-u5dral8oni.pdf

Context driven chinese string segmentation and recognition

This paper presents a lexicon based method for Chinese string recognition. In this method, we recognize a Chinese string image as a whole by matching it against lexicons in a database. We first over-segment the input line image into a series of radicals and recognize all the possible radical combinations. We then search for candidate lexicons in a given database according to the extracted keywords. Each lexicon is compared with the image to find the best match between the radicals and the given string by substring alignment. In this process, the segmentation and recognition results are determined synchronously. Our method is tested on 500 handwritten images of Chinese address and achieves a correct rate of 87% in address match.

Substring Alignment Method for Lexicon Based Handwritten Chinese String Recognition and Its Application to Address Line Recognition

In this paper, we describe a bi-gram driven method for automatic reading of Chinese handwritten mails. In destination address block (DAB) location, text lines are first extracted by connected components analysis. Each candidate line is segmented and recognized by our holistic method, which incorporates mail layout features, recognition confidence and context cost. All these are also taken into consideration to identify the DABs from the candidate text lines. Based on them, street address line and organization name line are determined. At last step, edit distance based string matching is performed against given databases. We also discuss the pretreatment to deal with Chinese address databases consisted of a large amount of vocabularies in order to generate keywords for fast indexing during matching. Detailed experiment results on handwritten mail samples are given in the last section.

/pdf/application-of-bi-gram-driven-chinese-handwritten-character-17olz5le68.pdf

Zheng Ren

Papers

A Novel Segmentation and Recognition Algorithm for Chinese Handwritten Address Character Strings

A hidden Markov model based segmentation and recognition algorithm for Chinese handwritten address character strings

Context driven chinese string segmentation and recognition

Substring Alignment Method for Lexicon Based Handwritten Chinese String Recognition and Its Application to Address Line Recognition

Application of bi-gram driven chinese handwritten character segmentation for an address reading system