A new benchmark on the recognition of handwritten Bangla and Farsi numeral characters

The recognition of Indian and Arabic handwriting is drawing increasing attention in recent years. To test the promise of existing handwritten numeral recognition methods and provide new benchmarks for future research, this paper presents some results of handwritten Bangla and Farsi numeral recognition on binary and gray-scale images. For recognition on gray-scale images, we propose a process with proper image pre-processing and feature extraction. In experiments on three databases, ISI Bangla numerals, CENPARMI Farsi numerals, and IFHCDB Farsi numerals, we have achieved very high accuracies using various recognition methods. The highest test accuracies on the three databases are 99.40%, 99.16%, and 99.73%, respectively. We justified the benefit of recognition on gray-scale images against binary images, compared some implementation choices of gradient direction feature extraction, some advanced normalization and classification methods.     

An image database of handwritten Bangla words with automatic benchmarking facilities for character segmentation algorithms

Neural Computing and Applications - Recognition of unconstrained handwritten word images is an interesting research problem which gets more challenging when lexicon-free words are considered....     

Recognition of Bangla compound characters using structural decomposition

In this paper we propose a novel character recognition method for Bangla compound characters. Accurate recognition of compound characters is a difficult problem due to their complex shapes. Our strategy is to decompose a compound character into skeletal segments. The compound character is then recognized by extracting the convex shape primitives and using a template matching scheme. The novelty of our approach lies in the formulation of appropriate rules of character decomposition for segmenting the character skeleton into stroke segments and then grouping them for extraction of meaningful shape components. Our technique is applicable to both printed and handwritten characters. The proposed method performs well for complex-shaped compound characters, which were confusing to the existing methods.     

A classifier for Bangla handwritten numeral recognition

This paper presents a novel pattern classification approach - a kernel and Bayesian discriminant based classifier which utilizes the distribution characteristics of the samples in each class. A kernel combined with Bayesian discriminant in the subspace spanned by the eigenvectors which are associated with the smaller eigenvalues in each class is adopted as the classification criterion. To solve the problem of the matrix inverse, the smaller eigenvalues are substituted by a small threshold which is decided by minimizing the training error in a given database. Application of the proposed classifier to the issue of handwritten numeral recognition demonstrates that it is promising in practical applications.     

Emergent componential coding of a handwritten image database by neural self-organisation

This paper demonstrates the unsupervised discovery of localised components in real image data, using images of much larger size than the small fragments from which components have previously been extracted. The handwriting images used are also much more homogeneous than the random natural scenes used in earlier demonstrations, containing components of a specific size-scale and structure. Because of this homogeneity, the components found are not wavelets covering a range of size scales: instead, they correspond to line- and curve-segments made by the pen. The objective function that is optimised here encodes and reconstructs the data via a Markov process, and is also related to density modelling techniques. Several earlier theoretical and experimental results can also be attributed to the form of neuron used here, including the extraction of words from continuous speech and the discovery of unknown transformation invariances via the controlled breaking of dynamical symmetry.     

A benchmark for unconstrained online handwritten Uyghur word recognition

International Journal on Document Analysis and Recognition (IJDAR) - Despite some interesting results from different research groups, a public database for Uyghur online handwriting recognition and...     

A database for handwritten text recognition research

An image database for handwritten text recognition research is described. Digital images of approximately 5000 city names, 5000 state names, 10000 ZIP Codes, and 50000 alphanumeric characters are included. Each image was scanned from mail in a working post office at 300 pixels/in in 8-bit gray scale on a high-quality flat bed digitizer. The data were unconstrained for the writer, style, and method of preparation. These characteristics help overcome the limitations of earlier databases that contained only isolated characters or were prepared in a laboratory setting under prescribed circumstances. Also, the database is divided into explicit training and testing sets to facilitate the sharing of results among researchers as well as performance comparisons     

A fuzzy substroke extractor for handwritten Chinese characters

This paper incorporates fuzzy technique into handwritten Chinese character substroke extraction. By computing a fuzzy score for every possible substroke, feature loss is reduced without imposing unnecessary restrictions on the writing style. Our extractor is as good as existing crisp substroke extractors in unambiguous situations, and it outperforms them in ambiguous cases in terms of the number of desirable substrokes extracted. Comparing with the “explore-every-possibility” approach based on crisp decision, we arrive at the correct feature set in a smaller number of iterations. The extractor's performance could also be tuned via a number of system parameters.     

Recognition of handwritten cursive Arabic characters

An automatic off-line character recognition system for handwritten cursive Arabic characters is presented. A robust noise-independent algorithm is developed that yields skeletons that reflect the structural relationships of the character components. The character skeleton is converted to a tree structure suitable for recognition. A set of fuzzy constrained character graph models (FCCGM's), which tolerate large variability in writing, is designed. These models are graphs, with fuzzily labeled arcs used as prototypes for the characters. A set of rules is applied in sequence to match a character tree to an FCCGM. Arabic handwritings of four writers were used in the learning and testing stages. The system proved to be powerful in tolerance to variable writing, speed, and recognition rate     

Experiments with adaptation strategies for a prototype-based recognition system for isolated handwritten characters

This paper describes an adaptive recognition system for isolated handwritten characters and the experiments carried out with it. The characters used in our experiments are alphanumeric characters, including both the upper- and lower-case versions of the Latin alphabets and three Scandinavian diacriticals. The writers are allowed to use their own natural style of writing. The recognition system is based on the k-nearest neighbor rule. The six character similarity measures applied by the system are all based on dynamic time warping. The aim of the first experiments is to choose the best combination of the simple preprocessing and normalization operations and the dissimilarity measure for a multi-writer system. However, the main focus of the work is on online adaptation. The purpose of the adaptations is to turn a writer-independent system into writer-dependent and increase recognition performance. The adaptation is carried out by modifying the prototype set of the classifier according to its recognition performance and the user's writing style. The ways of adaptation include: (1) adding new prototypes; (2) inactivating confusing prototypes; and (3) reshaping existing prototypes. The reshaping algorithm is based on the Learning Vector Quantization. Four different adaptation strategies, according to which the modifications of the prototype set are performed, have been studied both offline and online. Adaptation is carried out in a self-supervised fashion during normal use and thus remains unnoticed by the user. Received June 30, 1999 / Revised September 29, 2000     

A robust statistical set of features for Amazigh handwritten characters

The main problem in the handwritten character recognition systems (HCR) is to describe each character by a set of features that can distinguish it from the other characters. Thus, in this paper, we propose a robust set of features extracted from isolated Amazigh characters based on decomposing the character image into zones and calculate the density and the total length of the histogram projection in each zone. In the experimental evaluation, we test the proposed set of features, to show its performance, with different classification algorithms on a large database of handwritten Amazigh characters. The obtained results give recognition rates that reach 99.03% which we presume good and satisfactory compared to other approaches and show that our proposed set of features is useful to describe the Amazigh characters.     

Position-width-pulse algorithm for recognition of handwritten characters

An algorithm for the recognition of handwritten characters based on the position-width-pulse method of recognition of curves is presented in the article. An algorithm for transformation of characters into curves is presented and a recognition procedure described. Numerical estimators of the proximity S _w of curves that graphically map the image of characters to be recognized relative to printed characters used as reference characters are calculated. A conclusion that assigns a recognized character to a corresponding reference character is arrived at on the basis of the minimum value of S _w with specified reliability.     

Two-stage segmentation of unconstrained handwritten Chinese characters

Correct segmentation of handwritten Chinese characters is crucial to their successful recognition. However, due to many difficulties involved, little work has been reported in this area. In this paper, a two-stage approach is presented to segment unconstrained handwritten Chinese characters. A handwritten Chinese character string is first coarsely segmented according to the background skeleton and vertical projection after a proper image preprocessing. With several geometric features, all possible segmentation paths are evaluated by using the fuzzy decision rules learned from examples. As a result, unsuitable segmentation paths are discarded. In the fine segmentation stage that follows, the strokes that may contain segmentation points are first identified. The feature points are then extracted from candidate strokes and taken as segmentation point candidates through each of which a segmentation path may be formed. The geometric features similar to the coarse segmentation stage are used and corresponding fuzzy decision rules are generated to evaluate fine segmentation paths. Experimental results on 1000 Chinese character strings from postal mail show that our approach can achieve a reasonable good overall accuracy in segmenting unconstrained handwritten Chinese characters.     

A heuristic noise reduction algorithm applied to handwritten numeric characters

A new noise reduction algorithm based on window sizes varying from 3 × 3 to 5 × 5 is presented. The algorithm was applied to reduce black (‘1’-valued) noise specks from 1000 isolated handwritten numeric characters.     

笔迹图像中的单个汉字字符分割

提出了一套完整的针对单字的笔迹图像分割算法,选用不同的笔迹样本作了验证实验,对实现单字分割做了全面的阐述论证。将模板分割算法中的行分割、字分割、单字图像库建立和基于模板匹配的分割算法结合在一起,提高了算法的运算速度和精确度。利用50幅笔迹样本进行测试,92%的单字分割样本可以作为单字模板,应用模板匹配分割算法92%的样本可以实现单字提取。     

Analysis of stroke structures of handwritten Chinese characters

Most handwritten Chinese character recognition systems suffer from the variations in geometrical features for different writing styles. The stroke structures of different styles have proved to be more consistent than geometrical features. In an on-line recognition system, the stroke structure can be obtained according to the sequences of writing via a pen-based input device such as a tablet. But in an off-line recognition system, the input characters are scanned optically and saved as raster images, so the stroke structure information is not available. In this paper, we propose a method to extract strokes from an off-line handwritten Chinese character. We have developed four new techniques: 1) a new thinning algorithm based on Euclidean distance transformation and gradient oriented tracing, 2) a new line approximation method based on curvature segmentation, 3) artifact removal strategies based on geometrical analysis, and 4) stroke segmentation rules based on splitting, merging and directional analysis. Using these techniques, we can extract and trace the strokes in an off-line handwritten Chinese character accurately and efficiently.