English Character Recognition Research Articles

Handwritten alphabet classification in Tamil language using convolution neural network

Handwritten Alphabet Recognition can be defined as the way of detecting characters from images of Handwritten language alphabets. This is one of the important problems that can be solved by Convolution Neural Networks (CNN). Recent developments in CNN have made it possible to expand this problem area from English character recognition or Numbers recognition to Regional Languages character recognition, there has not been sufficient studies conducted in the domain of regional languages. This study has attempted to give deep learning approach to Tamil Handwritten Alphabets classification. This article aims to develop 3 models of CNN – THAC-CNN1, THAC-CNN2 and THAC-CNN3 to recognize Tamil Handwritten Alphabets and classify them based on its category. Our proposed models use a combination of benchmark dataset and a customized dataset which totals to over 2800 images of different Tamil alphabets after various data augmentation techniques. The proposed models are compared with a popular image classification pre-trained models - VGG-11 and VGG-16. We use the standard classification metric - accuracy to measure the performance of our proposed models. With our dataset and augmentation techniques, one of our models THAC-CNN1 achieves 97% accuracy on the training dataset and 92.5% accuracy on test dataset as opposed to 72% and 73.5% accuracy on training dataset and test dataset by pre-trained models.

Optimizing English character recognition: Enhanced accuracy through meta-heuristic algorithms

Optimizing English character recognition: Enhanced accuracy through meta-heuristic algorithms

Intelligent recognition of English characters based on point cloud data model and artificial intelligence recognition

Intelligent recognition of English characters based on point cloud data model and artificial intelligence recognition

Off-line English character recognition system

Purpose of the study: This paper aims to recognition of handwritten English characters in offline mode. It develops an efficient character recognition model avoiding large variations in handwriting by using better feature extraction techniques. 
 Methodology: The samples of characters are preprocessed by applying a sequence of operations in succession like Thickening, Thresholding, Filtering, and Thinning. Efficient features like Gradient features and Zonal features have been extracted. Gradient features are helpful to find out stroke information in the character whereas Zonal features detail out local information in a more précised way. Hidden Markov Model is the classifier. 
 Main Findings: Classification has been started with only a 5-state HMM model but it is observed that as the number of states of HMM model is increased, the corresponding recognition rate is also improved. Finally, with the 36 states HMM model we have got the expected result. This produces an overall average recognition rate of 92.6%. For the letters ‘A’ and ‘W’, the recognition rate is found to be very low, because of a lot of variations in writing style of these letters.
 Applications of this study: HMM is a flexible tool which is capable of absorbing variations in character images. The future works will be concentrated on improvement of recognition rate of such letters by finding some demarcating features and post processing. The proposed method can be well used in Natural Language Processing, Signature verification, Face recognition like other Pattern Recognition applications. 
 Novelty/Originality of this study: Preprocessing uses Median filter which removes all stray marks in samples and hence avoids any possibility of false pixels. The combination of Gradient features and Zonal features leads to a recognition accuracy of 92.6% which may be used by researchers in any other domains for the purpose of classification. The application of HMM will motivate the readers to use it for better results of classification.

Computational Technologies for IoT Networks Application in English Classroom Teaching

The purpose of this paper is to combine digital sensing technology with English character recognition in order to improve its overall effectiveness. In addition, this paper will comprehensively analyze the functional requirements of the digital system in teaching management and will clarify the functional goals that the digital system of teaching management needs to achieve. In addition, this study does an in-depth analysis of the many functions of the system and categorizes each of those functions into a number of distinct business operations. In addition, the English classroom teaching mode is improved with the help of machine learning, deep learning, and digital technology. In addition, an English classroom teaching system that is based on IoT Networks technology is constructed in this study. In addition to this, the study investigates the algorithmic flow of the functional structure modules of the system, develops each functional module of the system in detail, and elucidates the software design schemes at all levels connected to each functional module. In conclusion, the experimental research presented in this work validates the efficiency of the algorithm model presented in this paper. The findings of the study indicate that the digital English teaching system constructed in this work is effective, which supports the findings of the study.

Characterization of English Braille Patterns Using Automated Tools and RICA Based Feature Extraction Methods.

Braille is used as a mode of communication all over the world. Technological advancements are transforming the way Braille is read and written. This study developed an English Braille pattern identification system using robust machine learning techniques using the English Braille Grade-1 dataset. English Braille Grade-1 dataset was collected using a touchscreen device from visually impaired students of the National Special Education School Muzaffarabad. For better visualization, the dataset was divided into two classes as class 1 (1–13) (a–m) and class 2 (14–26) (n–z) using 26 Braille English characters. A position-free braille text entry method was used to generate synthetic data. N = 2512 cases were included in the final dataset. Support Vector Machine (SVM), Decision Trees (DT) and K-Nearest Neighbor (KNN) with Reconstruction Independent Component Analysis (RICA) and PCA-based feature extraction methods were used for Braille to English character recognition. Compared to PCA, Random Forest (RF) algorithm and Sequential methods, better results were achieved using the RICA-based feature extraction method. The evaluation metrics used were the True Positive Rate (TPR), True Negative Rate (TNR), Positive Predictive Value (PPV), Negative Predictive Value (NPV), False Positive Rate (FPR), Total Accuracy, Area Under the Receiver Operating Curve (AUC) and F1-Score. A statistical test was also performed to justify the significance of the results.

Handwritten English Alphabet Recognition

Abstract: Character recognition is a process by which a computer recognizes letters, figures, or symbols and turns them into a digital form that a computer can use. In moment’s terrain character recognition has gained a lot of attention in the field of pattern recognition. Handwritten character recognition is useful in cheque processing in banks, form recycling systems, and numerous further. Character recognition is one of the well- liked and grueling areas of exploration. In the unborn character recognition produce a paperless terrain. In this paper, we describe the detailed study of the being system for handwritten character recognition. We give a literature review on colorful ways used in offline English character recognition. Keywords: Character; Character recognition; Preprocessing; Segmentation; Point birth; Bracket; neural network; Convolution neural network.

English character recognition algorithm by improving the weights of MLP neural network with dragonfly algorithm

Character Recognition (CR) is taken into consideration for years. Meanwhile, the neural network plays an important role in recognizing handwritten characters. Many character identification reports have been publishing in English, but still the minimum training timing and high accuracy of handwriting English symbols and characters by utilizing a method of neural networks are represents as open problems. Therefore, creating a character recognition system manually and automatically is very important. In this research, an attempt has been done to incubate an automatic symbols and character system for recognition for English with minimum training and a very high recognition accuracy and classification timing. In the proposed idea for improving the weights of the MLP neural network method in the process of teaching and learning character recognition, the dragonfly optimization algorithm has been used. The innovation of the proposed detection system is that with a combination of dragonfly optimization technique and MLP neural networks, the precisions of the system are recovered, and the computing time is minimized. The approach which was used in this study to identify English characters has high accuracy and minimum training time.

English character recognition algorithm by improving the weights of MLP neural network with dragonfly algorithm

Character Recognition (CR) is taken into consideration for years. Meanwhile, the neural network plays an important role in recognizing handwritten characters. Many character identification reports have been publishing in English, but still the minimum training timing and high accuracy of handwriting English symbols and characters by utilizing a method of neural networks are represents as open problems. Therefore, creating a character recognition system manually and automatically is very important. In this research, an attempt has been done to incubate an automatic symbols and character system for recognition for English with minimum training and a very high recognition accuracy and classification timing. In the proposed idea for improving the weights of the MLP neural network method in the process of teaching and learning character recognition, the dragonfly optimization algorithm has been used. The innovation of the proposed detection system is that with a combination of dragonfly optimization technique and MLP neural networks, the precisions of the system are recovered, and the computing time is minimized. The approach which was used in this study to identify English characters has high accuracy and minimum training time.

Real-Time Arabic Text-Reading for Visually Impaired People

Visually impaired (VI) people suffer from many difficulties when accessing printed material using existing technologies. These problems may include text alignment, focus, accuracy, software processing speed, mobility, and efficiency. Current technologies such as flatbed scanners and OCR programs need to scan an entire page. Recently, VI people prefer mobile devices because of their handiness and accessibility, but they have problems with focusing the mobile camera on the printed material. In this paper, a real-time Arabic text-reading prototype for VI people is proposed. It is based on using a wearable device for a hand finger. It is designed as a wearable ring attached to a tiny webcam device. The attached camera captures the printed Arabic text and passes it to the Arabic OCR system. Finally, the recognized characters are translated into speech using the text-to-speech (TTS) technology. Experimental results demonstrate the feasibility of the proposed prototype. It achieved an accuracy of 95.86% for Arabic character recognition and 98.5% for English character recognition.

Handwritten Data Digitization Using an Anchor based Multi-Channel CNN (MCCNN) Trained on a Hybrid Dataset (h-EH)

To develop a holistic system for handwritten English character recognition for manually filled forms by systematically synthesising a robust handwritten textual character dataset for acceptable representation of handwriting. As part of this study, 572 copies of a form were filled by over 200 different individuals to introduce demographic variation. These forms were then scanned and each handwritten character in the forms was labelled and extracted using standard image processing techniques. The dataset of 84,712 character images created by this method (HW-dataset) comprised of both alphabetical and numerical characters. Three hybrid datasets (h-EH) were then formed by combining EMNIST datasets and the HW-dataset based on Digits (h-EHd - 329,668 character images), Alphabets (h-EHa - 163,085 character images) and a mixture of Digits and Alphabets (h-EHm - 189,586 character images). An anchor based image extraction technique was used in conjunction with a Multi-Channel CNN (MCCNN) model which was trained on three versions of h-EH, to automate the process of digitization of handwritten forms. The classification accuracies of the MCCNN for h-EHa, h-EHd and h-EHm are 93%, 96% and 93% respectively for test data. Models trained on only the EMNIST dataset perform poorly on test data. An anchor based object detection method used in conjunction with MCCNN trained on h-EH produces excellent results in digitising hand filled forms. Touch free solutions will gain prevalence due to the emergence of threat of fomites in the world. In such a space, manual handling of forms for the purpose of data entry, digitization and information handling will be considered as potential health and safety hazards. The solution presented in the current work uses a combination of models which is trained on a hybrid handwritten data set with high demographic variability. The model developed as part of this study is well suited for enabling touch free handling of documents.

Review on Transforming Approaches for English Character Recognition

Review on Transforming Approaches for English Character Recognition

Pioneer dataset and automatic recognition of Urdu handwritten characters using a deep autoencoder and convolutional neural network

Automatic recognition of Urdu handwritten digits and characters, is a challenging task. It has applications in postal address reading, bank's cheque processing, and digitization and preservation of handwritten manuscripts from old ages. While there exists a significant work for automatic recognition of handwritten English characters and other major languages of the world, the work done for Urdu lan-guage is extremely insufficient. This paper has two goals. Firstly, we introduce a pioneer dataset for handwritten digits and characters of Urdu, containing samples from more than 900 individuals. Secondly, we report results for automatic recog-nition of handwritten digits and characters as achieved by using deep auto-encoder network and convolutional neural network. More specifically, we use a two-layer and a three-layer deep autoencoder network and convolutional neural network and evaluate the two frameworks in terms of recognition accuracy. The proposed framework of deep autoencoder can successfully recognize digits and characters with an accuracy of 97% for digits only, 81% for characters only and 82% for both digits and characters simultaneously. In comparison, the framework of convolutional neural network has accuracy of 96.7% for digits only, 86.5% for characters only and 82.7% for both digits and characters simultaneously. These frameworks can serve as baselines for future research on Urdu handwritten text.

Use The Positive Selection Algorithm and The Negative Selection Algorithm in The English Character Recognition

Use The Positive Selection Algorithm and The Negative Selection Algorithm in The English Character Recognition

Zig-zag diagonal and ANN for English character recognition

Zig-zag diagonal and ANN for English character recognition

Handwritten English Character Recognition and translate English to Devnagari Words

In our proposed work we will accept the challenges of recognizing the words and we will work to win the challenge. The handwritten document is scanned using a scanner. The image of the scanned document is processed victimization the program. Each character in the word is isolated. Then the individual isolated character is subjected to “Feature Extraction” by the Gabor Feature. Extracted features are passed through KNN classifier. Finally we get the Recognized word. Character recognition is a process by which computer recognizes handwritten characters and turns them into a format which a user can understand. Computer primarily based pattern recognition may be a method that involves many sub process. In today’s surroundings character recognition has gained ton of concentration with in the field of pattern recognition. Handwritten character recognition is beneficial in cheque process in banks, form processing systems and many more. Character recognition is one in all the favored and difficult space in analysis. In future, character recognition creates paperless environment. The novelty of this approach is to achieve better accuracy, reduced computational time for recognition of handwritten characters. The proposed method extracts the geometric features of the character contour. These features are based on the basic line types that forms the character skeleton. The system offers a feature vector as its output. The feature vectors so generated from a training set, were then used to train a pattern recognition engine based on Neural Networks so that the system can be benchmarked. The algorithm proposed concentrates on the same. It extracts totally different line varieties that forms a specific character. It conjointly also concentrates on the point options of constant. The feature extraction technique explained was tested using a Neural Network which was trained with the feature vectors obtained from the proposed method.

Common constraints limit Korean and English character recognition in peripheral vision.

The visual span refers to the number of adjacent characters that can be recognized in a single glance. It is viewed as a sensory bottleneck in reading for both normal and clinical populations. In peripheral vision, the visual span for English characters can be enlarged after training with a letter-recognition task. Here, we examined the transfer of training from Korean to English characters for a group of bilingual Korean native speakers. In the pre- and posttests, we measured visual spans for Korean characters and English letters. Training (1.5 hours × 4 days) consisted of repetitive visual-span measurements for Korean trigrams (strings of three characters). Our training enlarged the visual spans for Korean single characters and trigrams, and the benefit transferred to untrained English symbols. The improvement was largely due to a reduction of within-character and between-character crowding in Korean recognition, as well as between-letter crowding in English recognition. We also found a negative correlation between the size of the visual span and the average pattern complexity of the symbol set. Together, our results showed that the visual span is limited by common sensory (crowding) and physical (pattern complexity) factors regardless of the language script, providing evidence that the visual span reflects a universal bottleneck for text recognition.

Handwritten Numeric and Alphabetic Character Recognition and Signature Verification Using Neural Network

Handwritten signature and character recognition has become challenging research topic due to its numerous applications. In this paper, we proposed a system that has three sub-systems. The three subsystems focus on offline recognition of handwritten English alphabetic characters (uppercase and lowercase), numeric characters (0 - 9) and individual signatures respectively. The system includes several stages like image preprocessing, the post-processing, the segmentation, the detection of the required amount of the character and signature, feature extraction and finally Neural Network recognition. At first, the scanned image is filtered after conversion of the scanned image into a gray image. Then image cropping method is applied to detect the signature. Then an accurate recognition is ensured by post-processing the cropped images. MATLAB has been used to design the system. The subsystems are then tested for several samples and the results are found satisfactory at about 97% success rate. The quality of the image plays a vital role as the images of poor or mediocre quality may lead to unsuccessful recognition and verification.

Comparison of Structure Based Models for Handwritten English Character Recognition

Comparison of Structure Based Models for Handwritten English Character Recognition

Off-Line English Character Recognition with Geometric Discretization

Off-Line English Character Recognition with Geometric Discretization