Identification Of Handwritten Characters Research Articles

Intelligent Handwritten Identification Using Novel Hybrid Convolutional Neural Networks – Long-short-term Memory Architecture

Handwritten character identification finds broad applications in document analysis, digital forensics, and human-computer interaction. Conventional methods encounter challenges in accurately deciphering a range of handwriting styles and variations. Consequently, investigating intelligent system for handwriting identification becomes crucial to enhance accuracy and efficiency. This paper introduces an innovative hybrid deep learning architecture, seamlessly integrating the strengths of convolutional neural networks (CNN) and long-short term memory (LSTM) within a one single framework. The combination of these structures enables the model to effectively capture both spatial features and temporal dependencies inherent in handwritten strokes, resulting in improved recognition performance. The proposed 2DCNN-LSTM algorithm has been tested on MNIST dataset. The proposed hybrid CNN-LSTM structure has been compared to conventional intelligent machine learning methods, and the results demonstrate the superior performance of the hybrid CNN-LSTM, showcasing heightened accuracy, sensitivity, specificity, and other evaluation metrics.

Enhanced ResNet-151-based fused features for optimized Bi-LSTM-DNN-aided handwritten character and digits recognition

Optical Character Recognition (OCR) is a method to convert a scanned photo of“handwritten character recognition (HCR) or printed character recognition (PCR)” into a form of digital text. HCR is a version of OCR, which is remarkably modeled to identify handwritten text, while PCR aims at printed text identification. The identification of handwritten characters and digits is more complicated as compared to PCR because of the diversities in human writing styles, stokes, thickness, and curves of characters. Similarly, achievements in several computer vision tasks consider the Convolutional Neural Networks (CNN) to give an end-to-end solution for HCR with huge success. However, the process of significant feature learning for the identification of images is complicated with little data. Hence, this paper aims to develop a new handwritten character and digit recognition model with the incorporation of a deep learning strategy. Initially, the data related to Indian languages are collected from the standard benchmark datasets. Then, the collected data are given into the feature extraction phase 1, where the ResNet 151 is used for extracting the feature set 1. Similarly, the data gathered are considered in the feature extraction phase 2, where the Optimal Ensemble Pattern extraction approach is developed with Local Binary Pattern (LBP), Local Gradient Patterns (LGP), Local Tetra Pattern (LTrP), and Local Vector Pattern (LVP) for extracting the significant patterns from the language data. These extracted patterns are given into the ResNet 151 for getting the feature set 2. Here, the features from ResNet 151 get optimized with the enhanced optimization algorithm with Fitness-based Sail Fish Optimizer (F-SFO). The obtained feature set 1 and optimal feature set 2 are concatenated for performing final recognition. At last, the HCR is done with the help of developed Bi-LSTM-DNN to achieve the enhanced and accurate recognition of handwritten characters of the Indian languages. The performances of character recognition are further improved with the parameter optimization in Bi-LSTM-DNN with the same enhanced F-SFO. Overall result analysis, the accuracy of the designed method attains 95.12%.

Handwritten Digit Recognition Based on the MNIST Dataset under PyTorch

Thanks to advancements in machine learning and artificial intelligence techniques, computers can now practice on data and learn from it in a manner that is similar to how the human brain works. Handwritten character and number identification has been one of the most pressing and fascinating subjects in pattern recognition and image processing. One of the most urgent and intriguing topics in pattern recognition and picture processing has been the identification of handwritten characters and numbers. As a crucial part of artificial intelligence, handwritten digit identification technology provides a vast array of application possibilities. The data demonstrates that, even though handwritten numbers are simply created with a few straightforward strokes, the appearance of numbers is more variable due to the various writing styles of each person. In this study, a deep learning framework-based upgraded LeNet-5 convolutional neural network model is used to build a handwritten number recognition model in Python. Automatic recognition of handwritten numbers will become the standard recognition technique if it can be applied to a wide range of industries, including banking and accounting, and hence save human costs.

A CNN based Approach for Handwritten Character Identification of Telugu Guninthalu using Various Optimizers

A CNN based Approach for Handwritten Character Identification of Telugu Guninthalu using Various Optimizers

Intelligent Feature Extraction for Handwritten Tamil Mantra Recognition in Slogans by using PCA based Deep Convolutional Neural Network

Offline handwritten identification of characters is a core problem in pattern matching. The main challenge for researchers in the identification of handwritten characters is inscribing individual styles. Tamil mantras identification is a challenging job due to many missing features in the mantras ' complex structure. The problem at hand is to break traditional hand-designed features. A new venture has been undertaken to automatically extract the complex features for recognition and classification from the complex structure bypassing the individual Tamil characters into the Convolutional Neural Network, a special type of deep learning network. The best convolutional model was chosen to improve efficiency by comparing different convolutional models that vary in activation functions, classifiers, and pooling functions. Principal Component Analysis (PCA) was used to select the top n eigenvectors from the image for better efficiency. So with the above trained best model with PCA for independent Tamil character images, handwritten Tamil fonts in the slogans (a group of characters) have been well recognized.

Review of methods for Handwritten Character Identification using Optical Character Recognition (OCR)

Review of methods for Handwritten Character Identification using Optical Character Recognition (OCR)

Odia Handwritten Vowel Recognition System using Slantlet Transform and Differential Evolution based Functional Link Artificial Neural Network Classifier

One of the challenging problems in optical character recognition (OCR) is the identification of handwritten characters. It has several complexities like missing part, size variation, added noise, media devices used, slant variation etc. The goal of this paper is to develop an effective system for recognition of Odia handwritten vowels using functional link artificial neural network (FLANN) classifier and differential evolution (DE) optimization technique. In this paper the emphasis is on preprocessing, feature extraction, feature reduction and classification with optimization technique. Various preprocessing operations like size normalization mean filtering and canny edge detection methods are carried out on the vowel images to enhance the quality. Slantlet transform (SLT) is applied for feature extraction and the extracted features are further reduced by principal component analysis (PCA) method. FLANN classifier is applied on the reduced feature vector and the weights of the FLANN are optimized with DE evolutionary technique. The system has achieved a good classification accuracy of 91.32% on the dataset.

Recognition of South Indian Language Numerals Using Minimum Distance Classifier

The recognition of handwritten and machine printed numerals have received extensive attention in pattern recognition. Indian handwritten character identification is slightly demanding because of the presence of character modifiers and also many documents consists of numerals which are both printed and handwritten. There are plenty of recognition systems developed for numeral recognition. Our aim is to develop a single framework or recognition system which could distinguish between numerals of various languages. Mixture of printed and handwritten character numerals in Indian context normally show up in documents such as application forms, postal mail, office letters and so forth. Generally, Humans can distinguish the difference in characters but to make a machine understand is challenging. Hence in order to make the machine understand numerals of different languages, this study has been carried out. The recognition rate of 87.96, 87.93, 86.93 and 89.16%, respectively has been achieved for Kannada, Telugu, Tamil and Malayalam numerals respectively.

Writer Identification for Offline Handwritten Kanji Characters Using Multiple Features

 Abstract—This paper presents a study on character features and recognizers used for writer identification of offline handwritten Kanji characters. It is shown that a combination of two global features, two local features, and majority voting as a recognizer is efficient for writer identification. We performed experiments using an offline Kanji character database containing one-hundred Kanji characters, each written by one-hundred writers, and fifty samples of each Kanji character for a given writer. The experimental results show that the identification rate is 7 points higher than the conventional method using a single feature and obtained an identification rate higher than 99% by using three character classes. Writer identification based on scanned images of handwritten characters is a useful biometric modality with applications in forensic and historical document analysis. Research on writer identification that uses online characters is widespread, but offline characters lack form for conveying dynamic information. Nevertheless, research on writer identification using offline characters has proposed many features to acquire useful information. We studied efficient character features and recognizers for writer identification of handwritten offline Kanji characters. Kanji consists of logographic Chinese characters adopted in Japanese writing. The text-dependent writer identification in our research uses a character recognition process before writer identification because, in text-dependent writer identification, a character class is assumed to be already recognized. Therefore, most of the research on text-dependent writer identification of handwritten characters has the following characteristics: employs features developed for character recognition, does not use multiple character features, and uses the local features of a character. In this paper, we propose efficient character features and a recognizer for text-dependent writer identification of a handwritten Kanji character (1).

Applying backpropagation neural networks to fringe analysis

Advances in image processing and optics technology, allied to the development of algorithmic techniques such as the fast Fourier transform and phase stepping, have allowed automatic fringe analysis to be successfully applied to many problems in visual inspection and noncontact surface measurement. However, when confronted with complicated or noisy images the algorithmic techniques tend to be less successful, implying an alternative approach may be necessary. Neural networks offer such an alternative. They have already been applied with some success to such conceptually similar pattern recognition problems, as the classification of fingerprints, the recognition of facial expressions and the identification of hand-written characters. Here, neural networks are applied to two simple fringe analysis problems. Firstly, to find the radius of a one-dimensional curved surface from its simulated fringe projection intensity distribution and, secondly, to identify four lensshaped objects of different radii of curvature from real fringe patterns obtained under different illumination conditions. In the first experiment, the backpropagation and radial basis function network paradigms are compared. In the second case, backpropagation is compared with the fuzzy-artmap paradigm. Performance criteria are the number of training data presentations, the accuracy of interpolation in the simulation experiment and the classification precision for the real data.