Wavelet-Based Recognition of Handwritten Characters Using Artificial Neural Network

This chapter reviews various elastic matching techniques for handwritten character recognition. Elastic matching is formulated as an optimization problem of planar matching, or pixel-to-pixel correspondence, between two character images under a certain matching model, such as affine and nonlinear. Use of elastic matching instead of rigid matching improves the robustness of recognition systems against geometric deformations in handwritten character images. In addition, the optimized matching represents the deformation of handwritten characters and thus is useful for statistical analysis of the deformation. This chapter argues the general property of elastic matching techniques and their classification by matching models and optimization strategies. It also argues various topics and future work related to elastic matching for emphasizing theoretical and practical importance of elastic matching.

This manuscript is focus on the utilization of object detection algorithm GLAC (Gradient Local Auto-Correlation) for the handwritten character recognition (HCR) problem. HOG and SIFT are already used in this (HCR) field except GLAC which produced good results than HOG and SIFT for object detection problem like human in images, pedestrian detection and image patch matching. This paper utilized GLAC algorithm to recognize the handwritten Devanagari characters. GLAC applied on two handwritten Devanagari databases, ISIDCHAR and V2DMDCHAR. The images of databases are also normalized with and without preserving aspect ratio. Using GLAC method and SVM classifier, the best results obtained on ISIDCHAR and V2DMDCHAR are 93.21%, 95.21 % respectively that justified the utilization of GLAC algorithm for character recognition problem.

Due to increased usage of digital technologies in all sectors and in almost all day to day activities to store and pass information, Handwriting character recognition has become a popular subject of research. Handwriting remains relevant, but people still want to have Handwriting copies converted into electronic copies that can be communicated and stored electronically. Handwriting character recognition refers to the computer's ability to detect and interpret intelligible Handwriting input from Handwriting sources such as touch screens, photographs, paper documents, and other sources. Handwriting characters remain complex since different individuals have different handwriting styles. This paper aims to report the development of a Handwriting character recognition system that will be used to read students and lectures Handwriting notes. The development is based on an artificial neural network, which is a field of study in artificial intelligence. Different techniques and methods are used to develop a Handwriting character recognition system. However, few of them focus on neural networks. The use of neural networks for recognizing Handwriting characters is more efficient and robust compared with other computing techniques. The paper also outlines the methodology, design, and architecture of the Handwriting character recognition system and testing and results of the system development. The aim is to demonstrate the effectiveness of neural networks for Handwriting character recognition.

In this paper, a new parallel compact integration scheme based on multi-layer perceptron (MLP) networks is proposed to solve handwritten Chinese character recognition (HCCR) problems. The idea of metasynthesis is applied to HCCR, and compact MLP network classifier is defined. Human intelligence and computer capabilities are combined together effectively through a procedure of two-step supervised learning. Compared with previous integration schemes, this scheme is characterized with parallel compact structure and better performance. It provides a promising way for applying MLP to large vocabulary classification.

This study is about the selection of classifiers in handwritten character recognition. The aim of the study is to determine the most appropriate classifier type for a given handwritten character feature vector. PCA based features were classified by both multilayer artificial neural networks (ANN) and support vector machines (SVM), and then the recognition results were compared. We selected error backpropagation, resilient backpropagation and scaled conjugate gradients as ANN training methods, while the SVM kernel types selected were linear, RBF and polynomial. The experimental results show that the SVM has better training and test performance than ANN.

In this paper we propose a Least Mean Square (LMS) algorithm for the practical training of the class of min-max classifiers. These are lattice-theoretic generalization of Boolean functions and are also related to feed-forward neural networks and morphological signal operators. We applied the LMS algorithm to the problem of handwritten character recognition. The database consists of segmented and cleaned digits. Features that were extracted from the digits include Fourier descriptors and morphological shape-size histograms. Experimental results using the LMS algorithm for handwritten character recognition are promising. In our initial experimentation, we applied the min-max classifier to binary classification of '0' and '1' digits. By preprocessing the feature vectors, we were able to achieve an error rate of 1.75% for a training set of size 1200 (600 of each digit); and an error rate of 4.5% on a test set of size 400 (200 of each). These figures are comparable to those obtained by 2-layer neural nets trained using back propagation. The major advantage of min-max classifiers compared to neural networks is their simplicity and the faster convergence of their training algorithm.

Hindi is the official language of India and used by a large population for several public services like postal, bank, judiciary, and public surveys. Efficient management of these services needs language-based automation. The proposed model addresses the problem of handwritten Hindi character recognition using a machine learning approach. The pre-trained DCNN models namely; InceptionV3-Net, VGG19-Net, and ResNet50 were used for the extraction of salient features from the characters’ images. A novel approach of fusion is adopted in the proposed work; the DCNN-based features are fused with the handcrafted features received from Bi-orthogonal discrete wavelet transform. The feature size was reduced by the Principal Component Analysis method. The hybrid features were examined with popular classifiers namely; Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM). The recognition cost was reduced by 84.37%. The model achieved significant scores of precision, recall, and F1-measure—98.78%, 98.67%, and 98.69%—with overall recognition accuracy of 98.73%.

Character recognition has been an active area of research and, due to its diverse applicable environment, it continues to be a challenging research topic. The data entry form is a convenient and successful tool for collection of information by filling the sheets with handwritten characters. For many purposes, such as documenting and archiving, extracting the handwritten characters is important. One of the most important fields in these forms is the data-filled boxes. The extraction process is important in processes of handwritten recognition techniques. Feature extraction plays an important role in different classification-based problems, such as face recognition, signature verification, optical character recognition (OCR), etc. The performance of OCR highly depends on the proper selection and extraction of a feature set. Different feature extraction methods are designed for different representations of the characters. The holistic approach is focused on feature extraction of the entire image and recognition using a neural network. This work proposes a subspace approach that regularizes and extracts Eigen features from handwritten numerals. Extracted Eigen features are recognized using a neural network. The proposed algorithm has been successfully implemented and has the added advantage of obtaining the extraction and recognition result at the same time.

BACKGROUND: Currently, artificial intelligence in the form of artificial neural networks is being actively implemented in a number of areas of our lives, including medicine. In particular, in otorhinolaryngology, artificial neural networks are used to analyze images obtained during endoscopic examinations of patients (e.g., videolaryngoscopy) [1–3]. The interpretation of laryngoscopic images often presents significant difficulties for practicing physicians, which reduces the frequency of detection of precancerous laryngeal diseases and contributes to the increase in the number of patients with stage III–IV laryngeal cancer [4, 5]. This underscores the significance of prompt performance and accurate interpretation of the findings of endoscopic examinations of patients with laryngeal disorders. Artificial neural networks can be employed to analyze the results of videolaryngoscopy, furnishing the physician with supplementary information that can enhance diagnostic accuracy and diminish the probability of error [6, 7]. AIM: The study aims to develop and train an artificial neural network for recognizing characteristic features of laryngeal neoplasms and variants of laryngeal normality. MATERIALS AND METHODS: The study was conducted under the grant of the Moscow Center for Innovative Technologies in Healthcare (grant No. 2112-1/22) entitled “Using Neural Networks (Artificial Intelligence Algorithms) for Control and Improving the Quality of Diagnosis and Treatment of Diseases of Laryngeal and Ear Structures through Digital Technologies”.The following methods were used during the course of the study: data collection for the creation of a photobank (dataset) of medical images obtained during videolaryngoscopy; data partitioning for the formation of datasets for individual nosologies and groups of diseases; the method of consilium; analysis of the accuracy of recognition and classification of digital endoscopic images; and training of classification neural networks. Consequently, a dataset comprising 1,471 laryngeal images in digital formats (JPEG, BMP) was assembled, labelled, and uploaded for the purpose of training the artificial neural network. Of the total number of images, 410 were classified as pertaining to laryngeal formation, while 1061 were classified as variants of normality. Subsequently, the neural network was trained and tested to identify the signs of normal and laryngeal masses. RESULTS: The results of the testing of the artificial neural network indicated the formation of an inaccuracy matrix, the calculation of the value of recognition accuracy, the calculation of the quality indicators of the model performance, and the construction of the ROC curve. The developed and trained artificial neural network demonstrated an accuracy of 86% in recognizing the signs of laryngeal masses and norms. CONCLUSIONS: This study demonstrates that a trained artificial neural network can successfully distinguish between signs of normal and laryngeal masses in endoscopic photographs. With further training of the neural network and achievement of high accuracy, this technology can be used in clinical practice as an assistant in the interpretation of laryngoscopic images and early diagnosis of laryngeal masses. It can also be employed to control and improve the quality of diagnosis and treatment of diseases of the throat, nose, and ears by primary care physicians.

This paper presents a novel technique for the supervised training of feed-forward artificial neural networks (ANN) using the Harmony Search (HS) algorithm. HS is a stochastic meta-heuristic that is inspired from the improvisation process of musicians. Unlike Backpropagation, HS is non-trajectory driven. By modifying an existing improved version of HS & adopting a suitable ANN data representation, we propose a training technique where two of HS probabilistic parameters are determined dynamically based on the best-to-worst (BtW) harmony ratio in the current harmony memory instead of the improvisation count. This would be more suitable for ANN training since parameters and termination would depend on the quality of the attained solution. We have empirically tested and verified our technique by training an ANN with a benchmarking problem. In terms of overall training time and recognition, our results have revealed that our method is superior to both the original improved HS and standard Backpropagation.

The conversion of handwritten text into machine-readable format is termed as Handwritten Character Recognition (HCR). The differences in size, design, and alignment angle of the Telugu and Kannada alphabets have difficulty in recognizing handwritten documents in these languages across various real-world applications. Newly developed machine learning and deep learning models provide a significant improvements in the handwritten text recognition. These innovative methods offer promising enhancements in the accuracy and efficiency of character recognition within handwritten documents. However, effective recognition of digits is not an easy task due to people’s varying writing styles in the input sample. To overcome such limitations, we explore a novel approach specifically designed to boost the performance of HCR in South Indian languages such as Kannada and Telugu. Initially, handwritten images are gathered using traditional data sources. These collected images are then given into the recognition phase. Here, an Adaptive Dilated convolution-based Deep Network (ADC-DeepNet) is developed for character identification purposes. In ADC-DeepNet, the ShuffleNetV2 blends with the Bidirectional Long Short-Term Memory (Bi-LSTM) to produce accurate results. This fusion provides effective character recognition. Here, the Iterative Concept of Lyrebird Optimization (ICLO) is newly proposed to optimize the variables from ADC-DeepNet to improve the character recognition efficacy. The efficiency of the HCR system is evaluated among several recent techniques with some performance measures. Finally, the outcome showed that the accuracy of the proposed approach is 95.6, and other models like CNN, ResNet, Convolutional Autoencoder, and DeepNet gave the accuracy of 88.8, 91.5, 90.6, and 93.3, respectively. Thus, the findings of the experiment show that the developed ADC-DeepNet model can effectively identify the handwritten characters in south Indian languages.

Joint A-SNN: Joint training of artificial and spiking neural networks via self-Distillation and weight factorization

This paper proposes a method to distinguish handwritten character regions from machine-printed ones using Hough transform (HT). The Gabor filtering in the human early vision realizes a type of Fourier transform (FT). Previously we proposed a FT-based distinction method successfully. However, we noticed simultaneously that the HT, instead of FT, may extract more features when we deal with characters which are regarded as piles of line segments. Experiments show that HT-based method, in combination with real-space features, achieves higher accuracy than the FT-based method. At the same time, the total calculation cost is found lower.

Research in offline handwriting recognition for unconstrained text remains a difficult challenge. Some problems such as noise in image, skew of text, cursive letters, and various handwriting style is still an open problem. Many method has been researched to solve those problems, such as k-NN, Neural Network, SVM and HMM. And to improve the recognition result, there are many methods can be implemented in the prerocessing stage. One of them is Kimura's method for slant correction that using chain code for character slant prediction. Those method consumes time and resource in its computation, meanwhile the accuracy is not improved significantly. Therefore, this paper propose to create handwritten character into graph with string representation based on structural approach. The purpose is to provide ability in improving recognition accuracy without relying on normalisation technique. The similarity distance between graphs measured using levenshtein distance. Experiment conducted to recognize handwritten upper-case letters and digits character images which taken from ETL-1 AIST databases. Levenshtein distance has an accuracy of 84.69% on digits and 67.01% on alphabet with 5% size of data for training and value 10 for string representation length. As a comparison, the Kimura's method are implemented for slant correction which results in a reduction of accuracy until 6%. Comparisons also made with some of previous work.

Incomplete handwritten Dongba character often appears in heritage documents and its recognition is significant for heritage and philology. However, all previous methods always suppose that a complete Dongba character is used as input, and thus fail to achieve satisfactory performance when applied to incomplete Dongba character recognition. In this paper, an end-to-end network (DB2RNet) is proposed for incomplete handwritten Dongba character image recognition by multiscale feature restoration. Specifically, we first develop datasets that contain different levels of incomplete Dongba characters. A restoration module is proposed to restore the input incomplete Dongba character, and then a recognition module is employed to recognize Dongba character. By introducing an inter-module residual connection between the restoration module and recognition module, the DB2RNet can strengthen feature information transmission and boost the recognition performance. In addition, novel multiscale feature blocks are introduced, which can provide more effective texture and contextual information transmission for Dongba character image restoration, and thus yielding better restoration effects and better recognition results. Extensive experiments are conducted on Dongba character, Chinese character and Oracle character datasets and validate the effectiveness, superiority and robustness of our methods. Experiments results demonstrate that our proposed DB2RNet achieves competitive Dongba character restoration and recognition performance and outperforms the current state-of-the-art methods.