Handwritten Recognition Research Articles

Survey Paper on Smart Note Taker

The Smart Note Taker is an innovative tool designed to maximize the entire process of note taking. By doing so, it enables users to make quick and effective notes either handwritten or voice-activated. Using handwriting recognition and voice-to-text technology, the digital tool transcribes handwritten or spoken information as editable digital text. With cloudbased storage and real-time synchronization, users will be able to access and manage their notes anywhere, at any time, and on multiple devices, thus ensuring the smooth integration of the platform into the workflows of the user. In addition, multimedia is supported, and users are enabled to add images, audio, and video to their notes. Moreover, the AI-powered features, such as automatic summarization, content relevance, and suggestions, help users increase productivity. The Smart Note Taker might be taken to be prepared for students, for working professionals and visually impaired users. It should, therefore, be most adapted to the needs of modern note-taking in terms of being convenient, flexible, and accessible

Full Page Handwriting Recognition on CUDA enabled Docker

-Handwritten text recognition is essential for document digitization but often struggles with multiline content. This paper presents an integrated approach using TrOCR, a pre-trained Transformer model, combined with PaddleOCR for enhanced text detection. The integration, optimized with GPU acceleration and multi-threading within a CUDA-enabled Docker environment, addresses the challenges of full-page handwriting recognition. A user-friendly Flask API with a Gradio demo was developed for deployment. Experimental results demonstrate that the system significantly improves the accuracy of multiline text recognition, outperforming existing models. This approach offers a scalable, efficient solution for accurate handwriting recognition in complex document layouts, advancing the field of document digitization.

Machine Learning Approach for Arabic Handwritten Recognition

Text recognition is an important area of the pattern recognition field. Natural language processing (NLP) and pattern recognition have been utilized efficiently in script recognition. Much research has been conducted on handwritten script recognition. However, the research on the Arabic language for handwritten text recognition received little attention compared with other languages. Therefore, it is crucial to develop a new model that can recognize Arabic handwritten text. Most of the existing models used to acknowledge Arabic text are based on traditional machine learning techniques. Therefore, we implemented a new model using deep machine learning techniques by integrating two deep neural networks. In the new model, the architecture of the Residual Network (ResNet) model is used to extract features from raw images. Then, the Bidirectional Long Short-Term Memory (BiLSTM) and connectionist temporal classification (CTC) are used for sequence modeling. Our system improved the recognition rate of Arabic handwritten text compared to other models of a similar type with a character error rate of 13.2% and word error rate of 27.31%. In conclusion, the domain of Arabic handwritten recognition is advancing swiftly with the use of sophisticated deep learning methods.

Enhancing the Recognition of Handwritten Arabic Characters through Hybrid Convolutional and Bidirectional Recurrent Neural Network Models

Handwritten Arabic character recognition remains a challenging task in pattern recognition due to the inherent complexities of the cursive script and visual similarities between characters. While deep learning techniques have demonstrated promising results in this domain, further enhancements to the model architecture can drive even greater performance improvements. This study introduces a hybrid deep learning approach that combines Convolutional Neural Networks (CNNs) with Bidirectional Recurrent Neural Networks, specifically Bidirectional Long Short-Term Memory (Bi-LSTM) and Bidirectional Gated Recurrent Units (Bi-GRU). By leveraging the strengths of both convolutional and recurrent neural network components, the proposed models are able to effectively capture spatial features as well as model the temporal dynamics and contextual relationships present in handwritten Arabic text. Experiments conducted on the AHCD and Hijjaa benchmark datasets show that the CNN-Bi-GRU framework achieved state-of-the-art accuracy rates of 97.05% and 91.78% respectively, outperforming previous deep learning-based methods. These results demonstrate the significant performance gains that can be achieved by integrating specialized temporal modeling and contextual representation capabilities into the handwriting recognition pipeline, without the need for explicit segmentation. The findings of this research represent a crucial advancement in the continued development of sophisticated and precise deep learning systems for Arabic handwriting recognition, with broad applications across domains that rely on efficient text extraction from handwritten documents.

Fine-grained Automatic Augmentation for handwritten character recognition

With the advancement of deep learning-based character recognition models, the training data size has become a crucial factor in improving the performance of handwritten text recognition. For languages with low-resource handwriting samples, data augmentation methods can effectively scale up the data size and improve the performance of handwriting recognition models. However, existing data augmentation methods for handwritten text face two limitations: (1) Methods based on global spatial transformations typically augment the training data by transforming each word sample as a whole but ignore the potential to generate fine-grained transformation from local word areas, limiting the diversity of the generated samples; (2) It is challenging to adaptively choose a reasonable augmentation parameter when applying these methods to different language datasets. To address these issues, this paper proposes Fine-grained Automatic Augmentation (FgAA) for handwritten character recognition. Specifically, FgAA views each word sample as composed of multiple strokes and achieves data augmentation by performing fine-grained transformations on the strokes. Each word is automatically segmented into various strokes, and each stroke is fitted with a Bézier curve. On such a basis, we define the augmentation policy related to the fine-grained transformation and use Bayesian optimization to select the optimal augmentation policy automatically, thereby achieving the automatic augmentation of handwriting samples. Experiments on five handwriting datasets of different languages demonstrate that FgAA achieves the best augmentation effect for handwritten character recognition. Our code is available at https://github.com/IMU-MachineLearningSXD/Fine-grained-Automatic-Augmentation

Handwritten text recognition of historical documents using deep neural network technologies

Handwritten text recognition of historical documents using deep neural network technologies

Ultrasensitive Flexible Strain Sensor Made with Carboxymethyl-Cellulose-Anchored Carbon Nanotubes/MXene for Machine-Learning-Assisted Handwriting Recognition.

The combination of wearable sensors with machine learning enables intelligent perception in human-machine interaction and healthcare, but achieving high sensitivity and a wide working range in flexible strain sensors for signal acquisition and accurate recognition remains challenging. Herein, we introduced carboxymethyl cellulose (CMC) into a carbon nanotubes (CNTs)/MXene hybrid network, forming tight anchoring among the conductive materials and, thus, bringing enhanced interaction. The silicone-rubber-encapsulated CMC-anchored CNTs/MXene (CCM) strain sensor exhibits an excellent sensitivity (maximum gauge factor up to 71 294), wide working range (200%), ultralow detection limit (0.05%), and outstanding durability (over 10 000 cycles), which is superior to most of the recently reported counterparts also based on a conductive composite film. Moreover, the sensor achieves seamless integration with human skin with the help of a poly(acrylic acid) adhesive layer, successfully obtaining stable and clear waveforms with meaningful profiles from the human body. On this basis, we proposed and realized a novel in-air handwriting recognition method via extracting multiple features of high-quality strain signals assisted by deep neural networks, achieving a high classification accuracy of 98.00 and 94.85% for Arabic numerals and letters, respectively. Our work provides an effective approach for significantly improving strain sensing performance, thereby facilitating innovative applications of flexible sensors.

Handwritten text recognition and information extraction from ancient manuscripts using deep convolutional and recurrent neural network

Handwritten text recognition and information extraction from ancient manuscripts using deep convolutional and recurrent neural network

Enhancing arabic handwriting recognition through optimized deep learning frameworks

Detecting Arabic handwriting is challenging due to letter shapes, intervening segments, and diacritical marks, despite recent advances in pattern recognition. Deep learning architectures ConvNeXt and NFNet-F5 and the meta-heuristic optimization algorithm Aquila Optimizer, inspired by eagle hunting, are used to overcome these challenges. We first review Arabic handwriting recognition literature to determine strengths, weaknesses, and future directions. Next, we describe Arabic handwriting, particularly its interconnectivity, diversity, and many diacritical symbols that make recognition difficult. The Aquila Optimizer is used to optimize CNN hyperparameters in this paper. The goal is to increase the recognition rate and reduce the computational workload. To prove the Aquila Optimizer's efficacy, the ConvNeXt and NFNet-F5 topologies are compared with and without it. The optimized ConvNeXt model has a recognition rate of 98.96%, significantly higher than comparable techniques. It also thoroughly assesses numerous meta-heuristic optimizers and highlights the Aquila Optimizer's potential to improve model performance. This work has enhanced Arabic handwriting recognition by building more precise and efficient models and provides a framework for future research on optimization and applying these models to other scripts like Persian and Urdu.

HTR-VT: Handwritten text recognition with vision transformer

We explore the application of Vision Transformer (ViT) for handwritten text recognition. The limited availability of labeled data in this domain poses challenges for achieving high performance solely relying on ViT. Previous transformer-based models required external data or extensive pre-training on large datasets to excel. To address this limitation, we introduce a data-efficient ViT method that uses only the encoder of the standard transformer. We find that incorporate a Convolutional Neural Network (CNN) for feature extraction instead of the original patch embedding and employ Sharpness-Aware Minimization (SAM) optimizer to ensure that the model can converge towards flatter minima yield notable enhancements. Furthermore, our introduction of the span mask technique, which masks interconnected features in the feature map, acts as an effective regularizer. Empirically, our approach competes favorably with traditional CNN-based models on small datasets like IAM and READ2016. Additionally, it establishes a new benchmark on the LAM dataset, currently the largest dataset with 19,830 training text lines. The code will be publicly available at: https://github.com/YutingLi0606/HTR-VT.

Deep learning-based digitization of Kurdish text handwritten in the e-government system

Many government institutions in developing countries such as the Kurdistan Region of Iraq (KRI) keep a variety of paper-based records that are available in printed or handwritten format. The need for technology that turns handwritten writing into digital text is therefore highly demanded. E-government in developed and developing countries is a crucial facilitator for the provision of such services. This paper aims to develop a deep learning model based on the mask region convolutional neural network (mask-RCNN) to effectively digitize kurdish handwritten text recognition (KHTR). In this research, typical datasets, which includes the isolated handwritten Central Kurdish character images, an extensive database of 40,410 images, and 390 native writers have been produced to determine the developed approach’s performance in terms of identification rates. This approach achieves adequate outcomes in terms of training time and accuracy. The proposed model gives higher performance for detection, localization, and recognition when using a dataset containing many challenges, the results were 80%, 96%, and 87.6 for precision, recall, and F-score respectively. The findings revealed that the proposed model obtained better results compared to other similar works. The accuracy of optical character recognition (OCR) is more than 99%.

Implementing handwritten text recognition using deep learning with TensorFlow: An MNIST dataset approach

Handwritten text recognition (HTR) is a pivotal technology with extensive applications in document digitization, postal automation, and educational tools. This paper delves into the implementing a deep learning-based system for recognizing handwritten digits using TensorFlow and the MNIST dataset. The MNIST dataset, a widely-used benchmark, comprises 60,000 training images and 10,000 testing images of handwritten digits, each standardized to a 28x28 pixel grayscale format. Leveraging the power of Convolutional Neural Networks (CNNs), our model effectively extracts features and classifies digits with high accuracy. The model architecture consists of multiple convolutional layers for feature extraction, pooling layers for dimensionality reduction, and fully connected layers for classification. Preprocessing steps include normalizing pixel values and one-hot encoding the labels, ensuring the data is optimally formatted for training. The TensorFlow framework, known for its robustness and scalability, facilitates the development and deployment of the model. Through a series of experiments, the model demonstrates impressive performance, achieving high accuracy on the MNIST test set. This paper underscores the potential of deep learning in handwritten text recognition. It sets the stage for future enhancements, such as recognizing more complex handwritten texts and optimizing the system for practical applications. The results highlight the effectiveness of deep learning techniques in overcoming the challenges associated with handwritten text recognition, paving the way for advanced, real-world implementations.

Evaluating Handwritten Character Recognition with Hu Moments and K-Nearest Neighbors Algorithm

Handwritten character recognition remains a challenging task in various domains, including handwriting analysis and document digitization. This task poses a significant challenge because of the variability and complexity of handwriting. To address this, it is crucial to develop different methodologies that enhance accuracy and efficiency in recognition. This research evaluates the discrimination capacity of a classifier that combines together the Hu Moments features extraction technique and the k-nearest neighbors (KNN) algorithm for the purpose of handwritten character recognition. The authors worked with handwritten characters from the English alphabet where each character is represented in two handwritten forms. The average accuracy of recognition was 82.69%, that conforms the efficiency of the proposed classifier. This research contributes to the field of automated handwritten recognition and highlights the necessity for improved techniques in character analysis, which has implications for handwriting recognition in different contexts, linguistic studies, document digitization, and other applications where character recognition is crucial. This is the first study that aims to apply the combination of Hu Moments features extraction technique in combination with the KNN algorithm for purpose of handwritten character recognition.

Enhancing Asian Indigenous Language Processing through Deep Learning-based Handwriting Recognition and Optimization Techniques

Asian indigenous language or autochthonous language is a language which is native to a region and spoken by indigenous people in Asia. This language is a linguistically different community created in the region. Recently, researchers in handwriting detection studies comparing with indigenous languages have attained important internet amongst the research community. A new development of artificial intelligence (AI), natural language processing (NLP), cognitive analytics, and computational linguistics (CL) find it helpful in the analysis of regional low-resource languages. It can be obvious in the obtainability of effectual machine detection methods and open access handwritten databases. Tamil is the most ancient Indian language that is mostly exploited in the Southern part of India, Sri Lanka, and Malaysia. Tamil handwritten Character Recognition (HCR) is a critical procedure in optical character detection. Therefore, this study designs a Henry Gas Solubility Optimization with Deep Learning-based Handwriting Recognition Model (HGSODL-HRM) for Asian Indigenous Language Processing. The proposed HGSODL-HRM technique relies on computer vision and DL concepts for automated handwriting recognition in the Tamil language, which is one of the popular indigenous languages in Asia. To accomplish this, the HGSODL-HRM technique employs a capsule network (CapsNet) model for feature vector generation with the HGSO algorithm as a hyperparameter optimizer. For the recognition of handwritten characters, wavelet neural network (WNN) model is exploited. Finally, the WNN parameters can be optimally chosen by sail fish optimizer (SFO) algorithm. To demonstrate the promising results of the HGSODL-HRM system, an extensive range of simulations can be implemented. The simulation outcomes stated the betterment of the HGSODL-HRM system compared to recent DL models.

Model For Handwritten Recognition Based On Artificial Intelligence

Model For Handwritten Recognition Based On Artificial Intelligence

Increasing Offline Handwritten Chinese Character Recognition Using Separated Pre-Training Models: A Computer Vision Approach

Offline handwritten Chinese character recognition involves the application of computer vision techniques to recognize individual handwritten Chinese characters. This technology has significantly advanced the research in online handwriting recognition. Despite its widespread application across various fields, offline recognition faces numerous challenges. These challenges include the diversity of glyphs resulting from different writers’ styles and habits, the vast number of Chinese character labels, and the presence of morphological similarities among characters. To address these challenges, an optimization method based on a separated pre-training model was proposed. The method aims to enhance the accuracy and robustness of recognizing similar character images by exploring potential correlations among them. In experiments, the HWDB and Chinese Calligraphy Styles by Calligraphers datasets were employed, utilizing precision, recall, and the Macro-F1 value as evaluation metrics. We employ a convolutional self-encoder model characterized by high recognition accuracy and robust performance. The experimental results demonstrated that the separated pre-training models improved the performance of the convolutional auto-encoder model, particularly in handling error-prone characters, resulting in an approximate 6% increase in precision.

Enhancing handwritten text recognition accuracy with gated mechanisms

Handwritten Text Recognition (HTR) is a challenging task due to the complex structures and variations present in handwritten text. In recent years, the application of gated mechanisms, such as Long Short-Term Memory (LSTM) networks, has brought significant advancements to HTR systems. This paper presents an overview of HTR using a gated mechanism and highlights its novelty and advantages. The gated mechanism enables the model to capture long-term dependencies, retain relevant context, handle variable length sequences, mitigate error propagation, and adapt to contextual variations. The pipeline involves preprocessing the handwritten text images, extracting features, modeling the sequential dependencies using the gated mechanism, and decoding the output into readable text. The training process utilizes annotated datasets and optimization techniques to minimize transcription discrepancies. HTR using a gated mechanism has found applications in digitizing historical documents, automatic form processing, and real-time transcription. The results show improved accuracy and robustness compared to traditional HTR approaches. The advancements in HTR using a gated mechanism open up new possibilities for effectively recognizing and transcribing handwritten text in various domains. This research does a better job than the most recent iteration of the HTR system when compared to five different handwritten datasets (Washington, Saint Gall, RIMES, Bentham and IAM). Smartphones and robots are examples of low-cost computing devices that can benefit from this research.

End-to-End Deep Learning Framework for Arabic Handwritten Legal Amount Recognition and Digital Courtesy Conversion

Arabic handwriting recognition and conversion are crucial for financial operations, particularly for processing handwritten amounts on cheques and financial documents. Compared to other languages, research in this area is relatively limited, especially concerning Arabic. This study introduces an innovative AI-driven method for simultaneously recognizing and converting Arabic handwritten legal amounts into numerical courtesy forms. The framework consists of four key stages. First, a new dataset of Arabic legal amounts in handwritten form (“.png” image format) is collected and labeled by natives. Second, a YOLO-based AI detector extracts individual legal amount words from the entire input sentence images. Third, a robust hybrid classification model is developed, sequentially combining ensemble Convolutional Neural Networks (CNNs) with a Vision Transformer (ViT) to improve the prediction accuracy of single Arabic words. Finally, a novel conversion algorithm transforms the predicted Arabic legal amounts into digital courtesy forms. The framework’s performance is fine-tuned and assessed using 5-fold cross-validation tests on the proposed novel dataset, achieving a word level detection accuracy of 98.6% and a recognition accuracy of 99.02% at the classification stage. The conversion process yields an overall accuracy of 90%, with an inference time of 4.5 s per sentence image. These results demonstrate promising potential for practical implementation in diverse Arabic financial systems.

Retraction Note: Effective offline handwritten text recognition model based on a sequence-to-sequence approach with CNN–RNN networks

Retraction Note: Effective offline handwritten text recognition model based on a sequence-to-sequence approach with CNN–RNN networks

Transcribing Medieval Manuscripts for Machine Learning

This article focuses on the transcription of medieval manuscripts. Whereas problems of transcription have long interested medievalists, few workable options in the era of printed editions were available besides normalisation. The automation of this process, known as handwritten text recognition (HTR), has made new kinds of digital text creation possible, but also has foregrounded the necessity of theorising transcription in our scholarly practices. We reflect here on different notions of transcription against the backdrop of changing text technologies. Moreover, drawing on our own research on medieval Latin Bibles, we present general guidelines for customizing transcription schemes, arguing that they must be designed with specific research questions and scholarly end use in mind. Since we are particularly interested in the scribal contribution to the production of codices, our transcription guidelines aim to capture abbreviations and orthographic variation between different textual witnesses for downstream machine learning tasks. In the final section of the article, we discuss a few examples of how the HTR-created transcriptions allow us to address new questions at scale in medieval manuscripts, such as textual variance across witnesses, the prediction of a change in scribal hands within a single manuscript as well as the profiling of individual and regional scribal characteristics.