With the development of artificial intelligence technology, many text generation models have emerged. People generate single words in various ways. For example, different forms of English characters and numbers can be generated through Generative Adversarial Network (GAN). Then, text lines can be synthesized from single words. However, most people only use some well-defined rules to synthesize "text line" from "single word". The result depends on people's experience. Therefore, the generated text lines often seem inconsistent, especially when generating handwriting text line. For example, it's hard to make the adjacent parts of the text look real. Moreover, adjacent words may have different styles. These problems can not be solved by simply modifying the text synthesis program. In this paper, we attempts to use neural networks to adjust the synthesized text lines. The input of our model is a synthetic text line, and then it can output a more coordinated text line. Four different loss and a discriminator ensure the quality of the generated text lines. In experiments, our neural network has successfully corrected many unnatural aspects of synthetic text lines. Considering that no one has tried to adjust the word at the text line level, our experiment offers novel and valuable insights.

Handwritten address recognition is very practical and popular in ICR (Intelligent Character Recognition) application field, especially for postal and express service. The structure of Japanese address has three components: high level, middle level and low level address. The high level and middle level address is very useful in postal applications since they indicate the destination of the express freight car. However, it's not easy to achieve high recognition accuracy on handwritten address merely by general ICR engine. In this paper, an efficient handwritten Japanese address recognition system is proposed that combines general ICR engine and special processing functions. Rough recognition result is obtained from a general ICR engine based on over-segmentation strategy. Special processing for precise address correction contains three main functions: FCN-based middle level address detection and recognition, BK-Tree-based high level address candidates searching and DTW-based accurate high level address matching. In the experiments on 670 handwritten Japanese address images, the proposed method successfully divided the total address into three levels, and achieved string-level correct rates of 81.6% and 72.5% in high level and middle level address respectively.

Blurring often happens when capturing documents with hand held cameras, which has negative effects on the Optical Character Recognition systems. In this paper, we propose a Convolutional Neural Network (CNN) based two-stage deblurring method. The method can deal with both real motion blur and focal blur situations, while it does not require exact estimation of the blur kernel. To achieve this, the whole blur kernel space is divided into several degradative sub-spaces. Firstly, a CNN classifier is trained to predict which sub-space the blurry image belongs to at the patch level. Then, several patches voting for the specific blur kernel sub-space is developed. Given the strong learning ability of CNN, only one CNN model corresponding to a degradative kernel sub-space is trained to restore the sharp images in the image restoration step. Experimental results show that the proposed approach performs well on the real blurring document images. In addition, we demonstrate that the proposed method could also handle the spatially-varying blurring.

Incompatibility of webpages under different browsers and platforms is a typical technical obstruction for webpage design. To address this issue, a key challenge is to automatically detect the incompatible components and quantitatively assess the distortion extent in cross-browser tests. This paper presents a new algorithm for image pair comparison from webpages, called iterative perceptual hash (IPH), as well as a new distortion evaluation index called structure-color-saliency (SCS). The IPH that operates in an iterative manner is proposed to detect content changes considering both global structure and local content difference. The SCS assesses the distortion extent in both dimensions of image structure and color and is capable of imitating the nonlinear human perception. Experiment results demonstrate the effectiveness of IPH (e.g., F1-score 96%) and the high consistency of SCS with subjective results.

Nowadays the CNN is widely used in practical applications for image classification task. However the design of the CNN model is very professional work and which is very difficult for ordinary users. Besides, even for experts of CNN, to select an optimal model for specific task may still need a lot of time (to train many different models). In order to solve this problem, we proposed an automated CNN recommendation system for image classification task. Our system is able to evaluate the complexity of the classification task and the classification ability of the CNN model precisely. By using the evaluation results, the system can recommend the optimal CNN model and which can match the task perfectly. The recommendation process of the system is very fast since we don't need any model training. The experiment results proved that the evaluation methods are very accurate and reliable.

With the development of deep learning, many difficult recognition problems can be solved by deep learning models. For handwritten character recognition, the CNN is used the most. In order to improve the performance of CNN, many new models have been proposed and in which the relaxation CNN [35] is widely used. The relaxation CNN has more complicated structure than CNN while the recognition time is the same with which. However, the training of relaxation CNN needs much more time than CNN. In this paper, we propose the cascading training for relaxation CNN. Our method can train a relaxation CNN of better performance while using almost the same training time with normal CNN. The experimental results proved that the relaxation CNN trained by cascading training is able to achieve the state-of-the-art performance on handwritten Chinese character recognition.

It is well known that the handwritten Chinese text recognition is a difficult problem since there are a large number of classes. In order to solve this problem, we proposed a whole new framework for unconstrained handwritten Chinese text recognition. The core module of the framework is the heterogeneous CNN trained by deep knowledge. The experimental results showed that our proposed method could achieve much better performance than the state-of-the-art methods (96.28% vs. 91.39% of CR on CASIA test set). Moreover, since the proposed framework is general, it can also be applied to other time sequence problems, such as speech recognition and video analysis.

The segmentation of touching characters is still a challenging problem in offline Chinese handwriting recognition. One feasible solution is through the over-segmentation strategy which maintains a high recall of correct cuts between adjacent characters and a moderate level of redundant cuts within a single character. Previous redundant cut filtering methods rely on either pure heuristics or learned geometric properties of correct cuts. In this work, we extend learning based cut filtering method from single cut level to cut sequence level by Hidden Markov Model (HMM). As a stochastic sequential modeling tool, HMM can utilize not only properties of individual cuts but also the left-to-right temporal context and spatial dependencies among a sequence of neighboring cuts. The experimental results on a large touching character dataset show that the proposed method is effective for over-segmentation and gives better performance than previous methods.

Automatic processing of large volume scanned Chinese bank statements is a urgent demand recently. Conventional methods can not well handle the following challenges of this problem: various layout styles, noises, and especially requirement of fast speed for large Chinese character set. This paper proposes a knowledge based table recognition method to meet fast speed requirement with good accuracy. Two kinds of knowledge are utilized to accelerate the identification of digit columns and the cell recognition: i) geometric knowledge about column alignment and quasi equal digit width, and ii) semantic knowledge about prior format based on the results from an optical character recognition (OCR) engine of digits. Experimental results on a real dataset show the effectiveness of our method.

Region segmentation is the key procedure in various text related image processing tasks. A good region extractor, which separates text area from complex background clutter, will reduce the burden of subsequent text grouping and post-processing functions. This paper propose a character region segmentation method based on a new concept named Stroke Stable Region (SSR) to achieve a better precision than many off-the-shelf region extractors such as MSER in the text segmentation task. Our work presented in this paper is inspired by the structure of MSER. However, instead of evaluating the area variation of each connected component, we proposed a novel parameter, stroke time, to measure the possibility that a pixel belongs to a character or a stroke by analyzing its character affinity. The experiments show that SSR tends to extract the visual objects with prominent text characteristics and is capable of suppressing various background noise. In a text extraction task on the ICDAR 2003 dataset, the SSR based method reduces the extracted noise components to about 1/3 of those obtained by the MSER based method, maintaining the same level of recall rate. The proposed algorithm was successfully applied to a wide range of text segmentation tasks.

Recently, the deep learning methods have achieved great success in pattern recognition tasks. Especially for character recognition, most of the state-of-the-art results belong to the deep learning models. Among those models, the convolutional neural network (CNN) becomes the most popular due to its outstanding performance. Therefore, many trials were made in order to make improvements on CNN. However, most of the trials only focused on the network structure or training skills, the inter-class information is usually ignored. In this paper, we have proposed a novel CNN model with two training feedbacks: the reconstruction feedback and the classification feedback. By using the reconstruction feedback, the inter-class information (for example, shape similarity) of the characters is taken into account. Consequently, without enlarging the network structure, our model can outperform those state-of-the-art improved CNN models, which is proved by the experimental results.

In practical applications of document understanding, if the documents have multiple languages and orientations, the conventional OCR systems can not be directly applied. This is because those OCR systems are usually designed for texts of single language and normal orientation. To solve this problem, many non-character based recognition approaches were proposed. However, the performance of those methods were not comparable with the mature OCR systems. Consequently, a better idea is to recognize the language type and orientation before the OCR is applied. Besides, the characters of different languages have very ambiguous shape, so it is very difficult to extract stable feature for the recognition. Recently, the convolutional neural networks (CNN) have achieved great success in pattern recognition tasks. Therefore, for such difficult tasks, the CNN is one of the best choice. In this paper, we first applied CNN to the recognition of the document properties. A novel sliding window voting process is proposed to reduce the network scale and fully use the information of the text line. In the experiments, our method had very high recognition rate. The results proved the advantage of the proposed method and which also can be applied to create a document understanding system with OCR systems.

In historical document images stitching, the arbitrary placement and the low quality of the historical paper always bring in uneven surface of the document, which might introduce local distortion to the image content. In these situations, some traditional methods may fail to find an optimal seam as they just use the content similarity. Some other methods tried to find two corresponding seams in both images, and the contents of the two images at the opposite sides of seams are transformed and stitched together. Although the image contents are correctly aligned with these methods, the transform may lead to huge content deformation and geometric inconsistency. To solve this problem, we proposed a seamless stitching method with shape deformation. In this method, one of the images is first deformed to approximate the other image, then single or dual optimal seams are estimated using an optimization method to minimize the deformation and to maximize the deformation consistency. With this method, the deformation of image content along the seam is consistent, and geometrically consistent elements such as straight lines can be well preserved. Experimental results show the effectiveness and efficiency of the proposed method.

Text line extraction in document images is an important prerequisite for many content based image understanding applications. In this paper, we propose an accurate and robust method for generic text line extraction, which can be applied on large categories of document images, diverse languages, and text lines with different orientations. Firstly, the candidate connected components are extracted from document image using Maximal Stable Extremal Region (MSER) with the noises filtered by Adaboost and Convolution Neural Network (CNN). Then, the coarse text lines are generated from hierarchical edges reconstruction and cut by local linearity of text lines in the document spanning tree. Finally, for accurate text line extraction, the cut multi-components are re-connected based on text line energy minimization in terms of text line consistency and the fitting error. Experimental results on multilingual test dataset demonstrate the effectiveness and robust of the proposed method, which yields higher performance compared with state-of-the-art methods.

Paper stitching technology can reconstruct a whole paper page from two sub-images separately scanned from a camera with limited vision field.

Deep learning methods have recently achieved impressive performance in the area of visual recognition and speech recognition. In this paper, we propose a hand- writing recognition method based on relaxation convolutional neural network (R-CNN) and alternately trained relaxation convolutional neural network (ATR-CNN). Previous methods regularize CNN at full-connected layer or spatial-pooling layer, however, we focus on convolutional layer. The relaxation convolution layer adopted in our R-CNN, unlike traditional convolutional layer, does not require neurons within a feature map to share the same convolutional kernel, endowing the neural network with more expressive power. As relaxation convolution sharply increase the total number of parameters, we adopt alternate training in ATR-CNN to regularize the neural network during training procedure. Our previous C- NN took the 1st place in ICDAR'13 Chinese Handwriting Character Recognition Competition, while our latest ATR-CNN outperforms our previous one and achieves the state-of-the-art accuracy with an error rate of 3.94%, further narrowing the gap between machine and human observers (3.87%).

Non-contact imaging devices such as digital cameras and overhead scanners can convert hardcopy books to digital images without cutting them to individual pages. However, the captured images have distinct distortions. A book dewarping system is proposed to remove the perspective and geometric distortions automatically from single images. A book boundary model is extracted, and a 3D book surface is reconstructed. And then the horizontal and vertical metrics of each column are restored from it. Experimental results show the good dewarping and speed performance. Since no additional equipments and no restrictions to specific book layouts or contents are needed, the proposed system is very practical in real applications.

This paper proposed a sub-structure learning based method for handwritten Chinese text recognition. In conventional methods, a standard character recognizer is trained on character classes only. Unreliable recognition results on character segments will decrease final recognition precision. By discovering stable sub-structure patterns from real character segment samples automatically, both character and sub-structure patterns are trained in character recognizer. The judgment reliability of segments being characters is significantly improved. Furthermore, to deal with millions of training segment samples, a two-stage clustering method is proposed for sub-structure learning. Experiment results on HIT-MW database show that the sub-structure learning based method improves performance significantly. The F1-measure evaluation of handwritten Chinese text recognition is improved by 8.84%.

This paper proposes a robust single-image super-resolution method for enlarging low quality camera captured text image. The contribution of this work is twofold. First, we point out the non-local reconstruction problem in neighbor embedding based super-resolution by statistical analysis on an empirical data set. Second, we introduce a local consistency constraint to explicitly regularize the linear reconstruction process, and adaptively generate the most possible candidates for the high-resolution image patch. For the non-consistent candidates, we rely on its adjacent overlapping patches for capability verification. Experimental results demonstrate that our solution produces visually pleasing enlargements for various text images.

Captions in videos are important and accurate clues for video retrieval. In this paper, we propose a fast and robust video caption detection and localization algorithm to handle low quality video images. First, the stroke response maps from complex background are extracted by a stoke filter. Then, two localization algorithms are used to locate thin stroke and thick stroke caption regions respectively. Finally, a HOG based SVM classifier is carried out on the detected results to further remove noises. Experimental results show the superior performance of our proposed method compared with existing work in terms of accuracy and speed.

In the real world, there are a huge amount of advertisement (ad) boards to make customers have a visual awareness of the products or services easily. However, information appearing in the ad boards is so limited that customers always want to know more ad details in a convenient way. In this paper, we present an mobile-based prototype system to automatically extract web ad information from images and websites. After capturing ad images by smartphones and sending them to a remote server, ad image text is recognized by OCR engine, from where ad phrases and keywords are extracted and combined together as queries. Ad web page candidates are then obtained by specific search engines and clustered to remove noises. OCR results are further used to estimate valid ad topic web pages which are pushed back to end users for searching more detailed ad information. Based on the experiments on a real-world ad image dataset collected by ourselves, true ad topic web pages can be found from top-one and top-ten returned pages in about 51.85% and 83.33% query images respectively, which illustrates the effectiveness of the proposed system.

In this paper, we present a novel method to recognize characters with severe perspective distortion using hash tables and perspective invariants. The proposed algorithm consists of storage and voting stages. With the help of perspective invariants, the combinations of 4-tuple bases for the perspective invariant coordinate system are searched out in an efficiently way. The bases are further selected so that the resulting transformation is effective. The characters' features under the perspective invariant coordinate system determine an entry in a one dimensional hash table, which is applied for storage and retrieval. Experimental results show the superior performance of the proposed method in comparison to other existing methods.

This paper presents a hybrid method for detecting and localizing texts in natural scene images by stroke segmentation, verification and grouping. To improve system performance, novelties on two aspects are proposed: 1) a scale-adaptive segmentation method is designed for extracting stroke candidates, and 2) a CRF model with pair-wise weight by local line fitting is designed for stroke verification. Moreover, color-based text region estimation is used to guide segmentation and verification more accurately. Experimental results on ICDAR 2005 competition dataset show that the proposed approach can detect and localize scene texts with high accuracy, even under noisy and complex backgrounds.

Document images captured by a mobile phone camera often have perspective distortions. In this paper, fast and robust vanishing point detection methods for such perspective documents are presented. Most of previous methods are either slow or unstable. Based on robust detection of text baselines and character tilt orientations, our proposed technology is fast and robust with the following features: (1) quick detection of vanishing point candidates by clustering and voting on the Gaussian sphere space, and (2) precise and efficient detection of the final vanishing points using a hybrid approach, which combines the results from clustering and projection analysis. The rectified image acceptance rate for Mobile Cam-based documents, signboards and posters is more than 98% with an average speed of about 100ms.

Text occlusion is among the most intractable obstacles for OCR engines. A typical example in document images is visible watermark characters, which are often occluded by foreground contents. This paper proposes a solution by restoring watermark characters before recognition. The text restoration process consists a core module as patch-based restoration method, which reconstructs the missing areas by referring to similar patches from undamaged areas. The filling sequence is in a order based on the structure complexity inside each patch, which helps to suppress reconstruction error propagation. Furthermore, the patch size is adaptively selected based on the local character stroke width. Experiments show that the proposed method produces good restoration quality and effectively improves the recognition rate of the following OCR process. Furthermore, the algorithm is optimized based on statistical analysis model and the processing time meets the real-time responding requirement.

In this paper, a rejection optimization method based on rejection threshold mapping is proposed. Different from conventional rejection methods which use the same rejection threshold for all samples, this technique utilizes the local information of samples to optimize the rejection threshold. The samples with the same class pair in the first two recognition candidates are treated as the same sample category. Based on the rejection distribution of the sample categories, the parameters of rejection threshold mapping of similar character pairs are learned and stored. When performing rejection, the corresponding mapping parameters are searched according to the class pair in the first two recognition candidates, and applied on the input threshold. The transformed threshold is used in final rejection decision. The experiments show that it is able to decrease error rate under same rejection rate on handwritten Chinese character recognition which verify its effectiveness.

Color plane separation is very useful in processing color document images. Many reported methods take it as a multi-class classification problem and work not well in overlapped color regions. This paper proposed a simple but effective linear projection based method for separating overlapped color planes. The separation task is taken as a probability problem, i.e., in the output plane, target color should have high response and the other colors should have low response, or vice versa. Furthermore, it assumes that the number of foreground colors is low, typically one to four, and overlapped areas contain mixed colors instead of opaque covering. Experimental results demonstrate the effectiveness and flexibility of our method.

In this paper, we propose a novel dual pass video stabilization system using iterative motion estimation and adaptive motion smoothing. In the first pass, the transformation matrix to stabilize each frame is returned. The global motion estimation is carried out by a novel iterative method. The intentional motion is estimated using adaptive window smoothing. Before the beginning of the second pass, we obtain the optimal trim size for a specific video based on the statistics of the transformation parameters. In the second pass, the stabilized video is composed according to the optimal trim size. Experimental results show the superior performance of the proposed method in comparison to other existing methods.

Watermark string detection is very useful for document security protection. In we proposed an image based document watermark detection system. In this paper, two major modifications are made based on the previous system.First, a trinary image mosaicing algorithm is proposed to merge the broken watermark string images into a high quality mosaic image. Second, we relax the conditions in the Maximum Clique based keyword detection algorithm.The new algorithm can handle watermark string with variant spacing. Therefore, not only English keywords, but also Japanese/Chinese keywords can be processed. The Experimental results show the effectiveness of our algorithm.

With rejection strategies in a handwriting recognition system, we are able to improve the reliability and accuracy of the recognized characters. In this paper, we propose several rejection strategies with multiple classifiers for handwritten character recognition. First, the rejection strategy for the single classifier is introduced, which is composed of three stages: initial scaling, confidence measure calculation, and rejection performing. Then, we analyze rejection strategies for multiple classifiers. We divided our rejection strategies into two categories: (1) for voting combination; and (2) for linear combination with multiple classifiers. In the voting combination style, three rejection strategies, OR, AND, and VOTING, are proposed. And for the linear combination one, rejection strategies for average and weighted combination are analyzed respectively. We also experiment and compare our rejection strategies with handwritten digit recognition.

The separation of Chinese character and English character is helpful for OCR technique. In this paper, a multi-level cascade classifier combined with feature selection is constructed to identify Chinese character and English character based on individual character. Most of samples are identified by the first node classifier, the remained low classification confidence samples are fed to the next node classifiers to get the final result. For the motivation of utilizing feature complementarity, each node classifier is trained on low classification confidence samples of its previous node classifier with independent feature selection. Furthermore, a confidence bias is utilized to improve the classifier generalization. The experiment results validate the effectiveness of this classifier.

A decomposition method for color document images is proposed in this paper. A two dimensional feature surface is constructed from the input color image, and then a novel and unsupervised method based on contour lines is proposed to segment the surface. In detail, colors of the image pixels are firstly projected to a spherical surface whose center is the background color. The projection is used to transform the observed colors to the corresponding 'ideal' foreground colors. Then the spherical surface is segmented into several non-overlapped regions, and each region corresponds to an individual layer of the input color document image. Finally, the image pixels are projected to the spherical surface and classified to the corresponding layers.

Document security is a very important topic in information management. In this paper, an image based watermark string detection system is proposed to detect the documents that include printed keyword strings as the watermark in the background. Therefore, the disclosure of the sensitive documents can be monitored automatically. Since the documents are represented in image format, the watermark string is detected by a parts based object recognition strategy. The two key contributions of our paper are cross validation based image registration and theMaximum Clique (MC) based object parts recognition. Experiments on PPT and WORD document pages with 5 different watermark keywords show the excellent performance of our system.

Caption detection in the video is an active research topic in recent years. In the conventional methods, one of most difficult problems is to effectively and quickly extract the durations of the different-size captions in the complex background. To solve this problem, a novel and effective method is presented to locate and track the captions in the video. The main contributions are: (1)present a multi-scale Harris-corner based method to detect the initial position of the caption (2)propose the SGF (Steady Global Feature) to determine the caption duration. Extensive experiments demonstrate the effectiveness of the proposed method.

Methods to rectify distortion of digital camera document images of curved papers have become important for camera-based image recognition. In this paper we propose a novel distortion rectification method based on "shape from parallel geodesies." This method considers the following features: parallel lines corresponding to character strings or ruled lines of tables on extended surface become parallel geodesies on a curved paper surface and a smoothly curved paper can be modeled by a ruled surface, that is, a sweep surface of rulings. The projected geodesies and the projected rulings exist in the input image derived from perspective transformation. The presented method extracts the projected geodesies, estimates the projected rulings in the input image, estimates the ruled surface that models the curved paper, and generates the corrected image, in this order. It can estimate the ruled surface model directly by numerical operations of differentiation, integration and matrix inversion without any iterative calculation. We also report on experiments that show the effectiveness of the proposed method.

Handwritten numeral recognition is an important pattern recognition task. It can be widely used in various domains, e.g., bank money recognition, which requires a very high recognition rate. As a state-of-the-art classifier, support vector machine (SVM), has been extensively used in this area. Typically, SVM is trained in a batch model, i.e., all data points are simultaneously input for training the classification boundary. However, some slightly exceptional data, only accounting for a small proportion, are critical for the recognition rates. Training a classifier among all the data may possibly treat such legal but slightly exceptional samples as "noise ". In this paper, we propose a novel approach to attack this problem. This approach exploits a two-stage framework by using difference features. In the first stage, a regular SVM is trained on all the training data; in the second stage, only the samples misclassified in the first stage are specially considered. Therefore, the performance can be lifted. The number of misclassifications is often small because of the good performance of SVM. This will present difficulties in training an accurate SVM engine only for these misclassified samples. We then further propose a multi-way to binary approach using difference features. This approach successfully transforms multi-category classification to binary classification and expands the training samples greatly. In order to evaluate the proposed method, experiments are performed on 10,000 handwritten numeral samples extracted from real banks forms. This new algorithm achieves 99.0% accuracy. In comparison, the traditional SVM only gets 98.4%.

Character degradation is a big problem for machine printed character recognition. Two main reasons for degradation are extrinsic image degradation such as blurring and low image dimension, and intrinsic degradation caused by font variations. A recognition method that combines two complementary classifiers is proposed in this paper. The local feature based classifier extracts the local contour direction changes, which is effective for character patterns with less structure deterioration. The global feature based classifier extracts the texture distribution of the character image, which is effective when the character structure is hard to discriminate. The two complementary classifiers are combined by candidate fusion in a coarse-to-fine style. Experiments are carried on degraded Chinese character recognition. The results prove the effectiveness of our method.

Document images captured by a mobile phone camera often have perspective distortions. Efficiency and accuracy are two important issues in designing a rectification system for such perspective documents. In this paper, we propose a new perspective rectification system based on vanishing point detection. This system achieves both the desired ef- ficiency and accuracy using a multi-stage strategy: at the first stage, document boundaries and straight lines are used to compute vanishing points; at the second stage, text base- lines and block aligns are utilized; and at the last stage, character tilt orientations are voted for the vertical vanish- ing point. A profit function is introduced to evaluate the reliability of detected vanishing points at each stage. If van- ishing points at one stage are reliable, then rectification is ended at that stage. Otherwise, our method continues to seek more reliable vanishing points in the next stage. We have tested this method with more than 400 images includ- ing paper documents, signboards and posters. The image acceptance rate is more than 98.5% with an average speed of only about 60ms.

We have previously proposed a unified presentation contents search mechanism named UPRISE (unified presentation slide retrieval by impression search engine), and have also proposed a method to use laser pointer information in lecture scene retrieval. In this paper, we discuss the treatment of the laser pointer and speech information, and propose two methods to filter the laser pointer information using keyword occurrence in slides and speech. We also propose weighting schemata with filtered laser pointer information using slide text and speech information. We evaluate our approach by using actual lecture videos and presentation slides

This paper proposes a combination method of character recognizer (CR) and word recognizer (WR) for Japanese handwritten words. CR is composed of character segmentation, isolated character recognition, and postprocessing. On the other hand, WR recognizes word images holistically based on a given word lexicon. These methods have been used as key modules in handwritten address recognition but have not been compared quantitatively up to now. In this paper, CR and WR are first empirically compared with each other using synthesized words generated from character images. Based on the comparison, a combination method is then proposed Experimental results show that the combination approach can achieve better recognition performance than the other two methods.

As the rapid progress of digital imaging technology, camera based character recognition receives more and more attentions. One challenge in camera based OCR is the recognition for degraded text. Conventional OCR engines usually recognize on binary image. However, the performance drops dramatically as the degradation level increases. In this paper, a new recognition method is proposed to recognize degraded character based on dual eigenspace decomposition and synthetic degraded data. Then, the degraded character string is segmented by the combination of binary and grayscale analysis. Experiments on single character and text string recognition prove the effectiveness of our method.

Images play a very important role in web content delivery. Many WWW images contain text information that can be used for web indexing and searching. A new text extraction and recognition algorithm is proposed in this paper. The character strokes in the image are first extracted by color clustering and connected component analysis. A novel stroke verification algorithm is used to effectively remove non-character strokes. The verified strokes are then used to build the binary text line image, which is segmented and recognized by dynamic programming. Since text in WWW image usually has close relationship with webpage content, approximate string matching is used to revise the recognition result by matching the content in the webpage with the content in the image. This effective post-processing not only improves the recognition performance, but also can be used in other applications such like image - webpage paragraph corresponding.

We present a method for automatically selecting the best filter to treat poor quality printed documents using image quality assessment. We introduce five quality measures to obtain information about the quality of the images, and morphological filters to improve their quality. A training set of 370 images was used to develop the system. Experimental results on the test set show a significant improvement in the recognition rate from 73.24% using no filter at all to 93.09% after applying a filter that was automatically selected.

We propose a high-accuracy ruled-line extraction method for digital camera images containing shadows. The conventional method that uses adaptive binarization has a problem in that light line segments become blurred due to the adverse effect of the adaptive binarization process. Then, we propose an accurate method which uses an intentionally collapsing binary image as a clue and exploits the linearity and gray level stability of each line segment. By the experiment, we demonstrated the effectiveness of the proposed method, which reduced the extraction error by half.

The global interpolation method we propose can extract a handwritten alpha-numeric character pattern even if it overlaps a border. Our method interpolates blank segments in a character after borders are removed by globally evaluating segment label connectivity and connectedness to produce characters with smooth edges. However, the method cannot interpolate missing superpositioning segments, such as an overlapping horizontal line in the number "2". To solve this problem, we propose a global interpolation method II which adds top-down recognition processing to the bottom-up processing of the existing global interpolation method by automatically acquiring knowledge of the relationship between the overlapped condition and recognition reliability. Experimental results which use generated overlapping characters using the ETL database showed that our global interpolation method II has almost the same accuracy as the original ETL database.

To improve recognition rate, it is important not only to utilize one character feature but personal handwriting characteristics. This paper realizes above approach based on our investigation result that characters written by the same writer have similar shapes and that there are several shapes even in the same category. In our method, clustering method is used to absorb the variance of character shapes in the category. First, character recognition for each character is executed. Next, misrecognized character candidates are extracted as isolated cluster by within-category clustering. Then, recognition results of the extracted characters are amended by between-category clustering which evaluates the distance between the cluster composed of misrecognized characters and the cluster composed of correctly recognized characters in every categories. Finally, experimental results shows that recognition rate is remarkably improved by our method.

The global interpolation (GIM) we propose evaluates segment pattern continuity and connectedness to produce characters with smooth edges while interpreting blank or missing segments, e.g., in extracting a handwritten character overlapping a border, correctly. Characters contacting a border, for example, are extracted after the border itself is labeled and removed. The absence of character segments is then interpolated based on segment continuity. Interpolated segments are relabeled and checked for matching against the original labeled pattern. If a match cannot be made, segments are reinterpolated until they can be identified. Experimental results show that global interpolation interprets the absence of character segments correctly and generates with smooth edges.>