Handwritten Bangla Numerals Research Articles

Improved low-cost recognition system for handwritten Bengali numerals

Handwriting recognition is very important due to its numerous potential applications. This paper is concerned about the low-cost features extraction for the development of an improved Bengali handwritten numeral recognition system. Each numeral was first resampled to a binary image of fixed size. A set of new features based on shape analysis was derived from the resampled image, and a multilayer neural network was trained using the extracted features. The recognition accuracy of the developed system was tested on both training and test sets of a publicly available Bengali handwritten numerals database at three different resolutions. Besides accuracy, the reliability of the system was also estimated using Cohen's kappa. The highest accuracy, 99.12% with reliability about 99%, was obtained for the test database at resolution of 32×32. The use of PCA reduces feature dimension from 142 to 68 resulting in a slight reduction in accuracy to 98.80%.

Improved low-cost recognition system for handwritten Bengali numerals

Improved low-cost recognition system for handwritten Bengali numerals

Convolutional neural networks performance comparison for handwritten Bengali numerals recognition

Handwritten recognition has drawn profound attention since decades ago due to its numerous potential applications in real life. Research on unconstrained handwritten recognition in some languages has achieved attractive advancement, but it lags behind for Bengali even though it is the major language spoken by about 230 million people in the Indian subcontinent, and even the first and official language of Bangladesh. Recently, the use of convolutional neural network (CNN) has been reported with high accuracy in pattern recognition and computer vision problems. The main purpose of this study is to provide an architecture of a CNN to improve the accuracy of handwritten Bengali numerals recognition (HBNR) and compare its performance with the existing ones. We proposed a new CNN architecture, VGG-11M, which improves an existing one (VGG-11). The normalized and rescaled images of each numeral were augmented by different transformation operations to increase the training samples and to add diversity in the dataset. Then, the images were used to train the proposed VGG-11M model. The recognition accuracy of the developed system was tested on both training and test sets of three publicly available handwritten Bengali numerals database at different resolutions. Finally the performance of the model was compared with four other architectures (LeNet-5, ResNet-50, VGG-11, and VGG-16). The highest accuracy 99.80%, 99.66%, and 99.25% was obtained using the proposed architecture on the test set of ISI, CMATERDB, and NUMTADB dataset, respectively, at resolution $$32\times {32}$$ . The proposed VGG-11M outperformed the existing architectures of CNN on HBNR.

Multiobjective optimization for recognition of isolated handwritten Indic scripts

Identifying the most informative regions of character images is important for a robust handwritten character recognition system. If it is to be done in an efficient and cost-effective way, this task becomes more challenging. In this work, we have proposed a new multi-objective optimization framework to identify those local regions cost-effective way. We define three objective functions to be taken into account for this purpose: 1) recognition accuracy. 2) average recognition time per character image, and 3) redundancy of the local regions. More specifically, in this work, a modified opposition-based multiobjective Harmony Search algorithm has been proposed to select the local regions from handwritten character images based on their rankings in a three-dimensional pareto-front. Our work has been evaluated on four datasets - 1) isolated handwritten Bangla Basic characters, 2) isolated handwritten Bangla numerals, 3) English numerals, and 4) isolated handwritten Devanagari characters. The results show a significant decrease in the recognition costs and redundancy and an increase in the recognition accuracy obtained on all the datasets. Thus, our system provides a cost-effective approach towards isolated handwritten character recognition.

A multi-objective approach towards cost effective isolated handwritten Bangla character and digit recognition

Identifying the most informative local regions of a handwritten character image is necessary for a robust handwritten character recognition system. But identifying them from a character image is a difficult task. If this task were to be performed incurring minimum possible cost, it becomes more challenging due to having two independent, apparently contradicting objectives which need to be optimized simultaneously, i.e. maximizing the recognition accuracy and minimizing the associated recognition cost. To address the problem a multi-objective approach is required. In the present task, two popular multi-objective optimization Algorithm (1) a Non-Dominated Sorting Harmony-Search Algorithm (NSHA) and (2) a Non-Dominated Sorting Genetic Algorithm-II (NSGA-II, Deb et al., 2002 [18]) are employed for region sampling separately. The method objectively selects the most informative set of local regions using the framework of Axiomatic Fuzzy Set (AFS) theory, from the sets of pareto-optimal solutions provided by the multi-objective region sampling algorithms. The system has been evaluated on two isolated handwritten Bangla datasets, (1) a dataset of randomly mixed handwritten Bangla Basic and Compound characters and (2) a dataset of handwritten Bangla numerals separately, with SVM based classifier, using a feature set containing convex-hull based features and CG based quad-tree partitioned longest-run based local features extracted from the selected local regions. The results have shown a significant increase in recognition accuracy and decrease in recognition cost for all the datasets. Thus the present system introduces a cost effective approach towards isolated handwritten character recognition systems.

Recognition of Handwritten Bangla Numerals using Adaptive Coefficient Matching Technique

This paper presents an approach to recognize handwritten bangla numerals from low pixel mobile camera based on correlation co-efficient. In this paper, an approach is identified to recognize such handwritten bangla numerals having different shape and scan through from any low pixel mobile device. It has also shown that while writing in continues sometimes two separated digit connected to each other, to distinguish such digit we used Line segmentation. High correlation coefficient will provide the successful results of recognition. A large dataset is used for training and testing purpose which gives overall 93.80% accuracy using mean of maximum correlation and 95.70% accuracy is shown when individually correlation found.

Hand Written Bangla Numerals Recognition for Automated Postal System

Recognition of handwritten Bangla numerals finds numerous applications in postal system automation, passports and document analysis and even for number plate identification. However, the recognition rate requires high and reliable accuracy for practical applications. This paper delineate a robust hybrid system for recognition of handwritten Bangla numerals for the automated postal system, which performed feature extraction using k-means clustering, Baye's theorem and Maximum a Posteriori, then the recognition is performed using Support Vector Machine . Recognition of handwritten numerals, such as postal codes, reveal all kinds of local and global deformations: distortions, different writing styles, thickness variations, wide variety of scales, limited amount of rotation, added noise, occlusion and missing parts. This paper shows that the proposed method is better than other system. Keywords - K-means clustering, Bayes' theorem, MAP, PCA, SVM and OCR.

Handwritten Bangla numeral recognition system and its application to postal automation

A recognition system for handwritten Bangla numerals and its application to automatic letter sorting machine for Bangladesh Post is presented. The system consists of preprocessing, feature extraction, recognition and integration. Based on the theories of principal component analysis (PCA), two novel approaches are proposed for recognizing handwritten Bangla numerals. One is the image reconstruction recognition approach, and the other is the direction feature extraction approach combined with PCA and SVM. By examining the handwritten Bangla numeral data captured from real Bangladesh letters, the experimental results show that our proposed approaches are effective. To meet performance requirements of automatic letter sorting machine, we integrate the results of the two proposed approaches with one conventional PCA approach. It has been found that the recognition result achieved by the integrated system is more reliable than that by one method alone. The average recognition rate, error rate and reliability achieved by the integrated system are 95.05%, 0.93% and 99.03%, respectively. Experiments demonstrate that the integrated system also meets speed requirement.