Handwritten Recognition Research Articles

Handwriting Recognition Implementation: A Machine Learning Approach

Handwritten text recognition, also referred to as handwritten character recognition, is a field of study that combines model recognition, computer vision, and artificial intelligence. In order to translate handwritten letters into relevant text and computer commands in real time, handwriting recognition systems use pattern matching. The properties of photographs and touch-screen devices can be acquired, detected, and converted into a machine-readable form by an algorithm that recognizes handwriting. An ensemble of bagged classification trees is one way to accomplish this. A bagged classification tree is an ensemble learning technique that helps to increase the efficiency and accuracy of machine learning algorithms by lowering the variance of a prediction model and addressing bias-variance trade-offs. The standard Kaggle digits dataset from (0-9) was utilised in this study to identify handwritten digits using a bagged classification method. And with an accuracy level of 0.8371, we finally came to a conclusion about the importance of the bagged classification strategy.

Context‐Aware TinyML Model for Korean Handwriting Recognition Under Online Assisted Learning Scenes

ABSTRACTOptical character recognition (OCR) based on wearable devices plays an important role in online learning. Although the existing convolutional recurrent neural network (CRNN) has achieved great success in the task of optical character recognition (OCR), this model cannot fully achieve global contextual information modeling. With the development of artificial intelligence IoT (AIoT) technology, existing deep models are difficult to deploy on resource‐constrained mobile devices. Therefore, this article proposes an effective context‐aware TinyML model for Korean handwriting recognition. Specifically, we effectively improve the global context modeling capability by embedding the Mamba layer in CRNN with linear computational complexity. In addition, we achieved model compression by introducing a distillation mechanism based on a multi‐layer joint distillation mechanism. A large number of experimental results on two publicly available datasets of Korean characters show that our proposed model achieves a higher performance.

An intelligent hybrid-fabric wristband system enabled by thermal encapsulation for ergonomic human-machine interaction

Human-machine interaction has emerged as a revolutionary and transformative technology, bridging the gap between human and machine. Gesture recognition, capitalizing on the inherent dexterity of human hands, plays a crucial role in human-machine interaction. However, existing systems often struggle to meet user expectations in terms of comfort, wearability, and seamless daily integration. Here, we propose a handwriting recognition technology utilizing an intelligent hybrid-fabric wristband system. This system integrates spun-film sensors into textiles to form the smart fabric, enabling intelligent functionalities. A thermal encapsulation process is proposed to bond multiple spun-films without additional materials, ensuring the lightweight, breathability, and stretchability of the spun-film sensors. Furthermore, recognition algorithms facilitate precise accurate handwriting recognition of letters, with an accuracy of 96.63%. This system represents a significant step forward in the development of ergonomic and user-friendly wearable devices for enhanced human-machine interaction, particularly in the virtual world.

Noncontact monolayered triboelectric nanogenerator based on stretchable MWCNTs/MXene/Ecoflex film for human–machine interface and high-accuracy handwritten recognition

Noncontact monolayered triboelectric nanogenerator based on stretchable MWCNTs/MXene/Ecoflex film for human–machine interface and high-accuracy handwritten recognition

Reading Georgian Manuscripts Automatically on the eScriptorium Platform

The article outlines the development of means for an automatic reading of Georgian manuscripts on the eScriptorium platform and the first results achieved with them. After an overview of the efforts undertaken in applying Optical Character Recognition (OCR) to Georgian printed books since the late 1980’s and a short introduction into the basics of the eScriptorium approach to Handwritten Text Recognition (HCR) and its functionalities, it exemplifies the application of the three core procedures of eScriptorium, which consist in the automatic segmentation of text-covered regions and lines, the automatic transcription of the detected lines based on manual input and the training of appropriate models, and the alignment with existing electronic texts in order to provide reliable ground truth for further training. With a total of 292 manually transcribed pages and 3812 pages with aligned (but not yet always corrected) text that have been processed so far, there is a strong material basis for further improvement of the models and the reading results depending on them.

Hybrid Intelligence: Filtering and Deep Learning for Handwritten Text Recognition

Hybrid Intelligence: Filtering and Deep Learning for Handwritten Text Recognition

A Comparative Study of Four Handwritten Text Recognition Models in Arabic Script

A Comparative Study of Four Handwritten Text Recognition Models in Arabic Script

Efficient deep learning models for Telugu handwritten text recognition

<p>Optical character recognition (OCR) technology is indispensable for converting and analyzing text from various sources into a format that is editable and searchable. Telugu handwriting presents notable challenges due to the resemblance of characters, the extensive character set, and the need to segment overlapping characters. To segment the overlapping characters, we assess the width of small characters within a word and segment the overlapping characters accordingly. This method is well suited for the segmentation of overlapping compound characters. To address the recognition of similar characters with less training periods we have used ResNet 18 and SqueezeNet models which have achieved character recognition rates of 95% and 94% respectively.</p>

Highly stretchable, self-healing, adhesive, 3D-printable and antibacterial double-network hydrogels for multifunctional wearable sensors.

Conductive hydrogels based on sodium alginate (SA) have potential applications in human activity monitoring and personal medical diagnosis due to their good conductivity and flexibility. However, most sensing SA-hydrogels exhibit poor mechanical properties and lack of self-healing, self-adhesive, and antibacterial properties, greatly limiting their practical applications. Therefore, in this paper, a multifunctional double-network PAA-SA hydrogel consisting of poly(acrylic acid) (PAA) and sodium alginate (SA) was prepared by a simple strategy. As a rigid network structure, SA endowed the hydrogel double network structure with excellent mechanical performance. As a wearable sensor, the PAA-SA hydrogel exhibited excellent tensile properties (strain: 1799.2 %), self-healing, high sensitivity (GF = 9.9), reliable repeatability, self-adhesive, 3D printability and antibacterial activity. Additionally, the highly sensitive wearing sensing PAA-SA hydrogel could accurately and real-time monitor various intense or subtle human movements, such as joint bending, face and throat vibration. Moreover, PAA-SA hydrogels were not only used for handwritten recognition of Arabic numerals and English letters, but also for real-time sensing of temperature changes and monitoring of human sweating. The prepared multifunctional wearable sensing hydrogel has the advantages of simple and versatile methods and low cost, making it a promising candidate for applications in different fields such as electronic skin, soft robotics, and medical monitoring.

Everyone Leaves a Trace: Exploring Transcriptions of Medieval Manuscripts with Computational Methods

Abstract The topic of this paper is a thirteenth-century manuscript from the French National Library (Paris, BnF français 24428) containing three popular texts: an encyclopedic work, a bestiary and a collection of animal fables. We have automatically transcribed the manuscript using a custom handwritten text recognition (HTR) model for old French. Rather than a content-based analysis of the manuscript’s transcription, we adapt quantitative methods normally used for authorship attribution and clustering to the analysis of scribal contribution in the manuscript. Furthermore, we explore the traces that are left when texts are copied, transcribed and/or edited, and the importance of that trace for computational textual analysis with orthographically unstable historical languages. We argue that the method of transcription is fundamental for being able to think about complex modes of authorship which are so important for understanding medieval textual transmission. The paper is inspired by trends in digital scholarship in the mid-2020s, such as public transcribe-a-thons in the GLAM (Galleries, Libraries, Archives and Museums) sector, the opening up of digitized archival collections with methods such as HTR, and computational textual analysis of the transcriptions.

Balochi Text Segmentation for Establishing Balochi OCR

OCR is considered the fastest way of data entry; the smart conversion of the text data is called handwritten text recognition. Many of the languages possess OCRs and there are still some languages lacking the OCR. Balochi is one of the national languages of the Pakistan country and the most of speakers live in Baluchistan province of Pakistan. Balochi computing is at its infancy and require attention to its many of the approaches to accumulate the level of other languages especially pertaining to the matter of computation. This paper investigates the relation between other Arabic adopting languages and proposes a segmentation algorithm to segment Balochi text paragraphs into lines, lines into words and words into characters. The algorithm has been adopted and fine tuned to produce the accuracy of 95%. The segmentation algorithm will play a role in developing a complex OCR and handwritten recognition of Balochi language.

Performance Comparison of Online Handwritten Telugu Character Recognition Using Various Local and Global Features

As the text is written on a special digitizer or Personal Digital Assistance (PDA) in which a sensor picks up the pen-tip movements along with the pen-up/pen-down switching, its automatic conversion is performed in the online Handwriting Recognition (HR). There are several works related to the online recognition of Devanagari as well as Tamil scripts. Meanwhile, the online recognition works associated with other Indian languages, specifically Telugu, which is complex in its structure together with style, are very few. Our work emphasizes the development of an online handwritten Telugu character recognition system using dominant points with the combination of SVM and performance analysis of various other features. Three classifiers namely, SVM, K-NN and MLP are used to examine the performance of the feature vectors. The proposed research is verified with HP-Lab data available in the UNIPEN format.

Alphabet Handwriting Recognition: From Wood-Framed Hydrogel Arrays Design to Machine Learning Decoding.

Handwriting recognition is a highly integrated system, demanding hardware to collect handwriting signals and software to deal with input data. Nonetheless, the design of such a system from scratch with sustainable materials and an easily accessible computing network presents significant challenges. In pursuit of this goal, a flexible, and electrically conductive wood-derived hydrogel array is developed as a handwriting input panel, enabling recognizing alphabet handwriting assisted by machine learning technique. For this, lignin extraction-refill, polypyrrole coating, and polyacrylic acid filling, endowing flexibility, and electrical conduction to wood are sequentially implemented. Subsequently, these woods are manufactured into a 5×5 array, creating a matrix of signals upon handwriting. Efficient handwritten recognition is then achieved through appropriate manual feature extraction and algorithms with low complexity within a computing network, as demonstrated in this work, the strategic choice of expertise-based feature engineering and simplified algorithms effectively boost the overall model performance on handwriting recognition. With potential adaptability, further applications in customized wearable devices and hands-on healthcare appliances are envisioned.

EGO-LM: An efficient, generic, and out-of-the-box language model for handwritten text recognition

The language model (LM) plays a crucial role in post-processing handwritten text recognition (HTR) by capturing linguistic patterns. However, traditional rule-based LMs are inefficient, and recent end-to-end LMs require customized training for each HTR model. To address these limitations, we propose an Efficient, Generic, and Out-of-the-box Language Model (EGO-LM) for HTR. To unlock the out-of-the-box capability of the end-to-end LM, we introduce a vision-limited proxy task that focuses on visual-pattern-agnostic linguistic dependencies during training, enhancing the robustness and generality of the LM. The enhanced capabilities also enable EGO-LM to iteratively refine its output for a further accuracy boost without additional tuning. Moreover, we introduce a Diverse-Corpus Online Handwriting dataset (DCOH-120K) with more diverse corpus types and more samples than existing datasets, including 83,142 Chinese and 39,398 English text lines. Extensive experiments demonstrate that EGO-LM can attain state-of-the-art performance while achieving up to 613× acceleration. The DCOH-120K dataset is available at .

Single-crystal ferroelectric LiNbO3 thin film-based synaptic devices enabled with tunable domain wall current for neuromorphic computing

Neuromorphic devices can emulate the human brain to process information, which receives lots of attention in the field of artificial intelligence. Synaptic devices based on ferroelectric thin films feature low-power consumption, multifunctionality, and scalability. Among them, ferroelectric charged domain wall (CDW) devices have attracted intensive interest for the implementation of memristive devices due to their ultrahigh integration ability inherited from the nanoscale domain wall thickness. In particular, the preparation of wafer-scale single-crystalline ferroelectric thin films via ion-sliced heterogeneous wafer bonding lays a good foundation for large-scale integration of ferroelectric devices with functional circuits. However, the biomimic synaptic characteristics and the systematic demonstration of synaptic devices are largely unexplored for this material system. Here, we demonstrate a model synaptic device based on a single-crystal ferroelectric LiNbO3 thin film, which provides the desired characteristics for neuromorphic computing. The conductance modulation demonstrates good linearity for efficient neuromorphic computing applications. Simulations using the Modified National Institute of Standards and Technology handwritten recognition dataset prove that LiNbO3-based synaptic devices can operate with an online learning accuracy of 95.1%. The injection and annihilation of the CDW are proposed as the basis of the conductivity modulation by combining with the piezoresponse force microscopy and conductive atomic force microscopy mapping measurements. With the mature fabrication process of the ultrathin high-quality ferroelectric thin films, LiNbO3-based synaptic devices have an extensive application prospect for future neuromorphic computing systems.

Handwritten Text Recognition: A Survey of OCR Techniques

Optical Character Recognition of handwritten texts has witnessed remarkable advancements with the integration of deep learning and machine learning techniques. Recognizing handwritten characters poses unique challenges due to script variability, linguistic diversity, and the complexities of historical documents. This survey explores recent developments in OCR for various languages, emphasizing innovative approaches such as Convolutional Neural Networks, attention mechanisms, and transfer learning. We analyze methodologies that enhance character recognition accuracy across languages, including Amharic, Arabic, Uchen Tibetan, Devanagari, and Tamil, while addressing resource efficiency in scene text recognition. Furthermore, the paper discusses advanced techniques for multilingual numeral recognition and writer identification in Indic scripts, highlighting cutting-edge strategies that push the boundaries of OCR technology. By synthesizing findings from the latest literature, this review provides valuable insights into ongoing challenges and future research directions in handwritten text recognition.

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

Von OCR und HTR bis NER und LLM

Abstract The presence of the digital is closely linked to the expectation that work processes will be made easier and faster. Considering the barely recognizable increase in speed in editing projects, this article deals with the potentials and challenges of digital tools and processes which, at least in theory, have the potential to speed up editing processes by transferring tasks to the machine that would otherwise be carried out by humans. Using the examples of optical character recognition, handwritten text recognition, named entity recognition and LLM-based dialog systems (AI-based chatbots), the article examines what is associated with the integration of such digital methods and tools into the editing workflow and to what extent the work and transformation processes and, in particular, the roles of human editors and their specific tasks change.

Ancient Yi Script Handwriting Sample Repository

The ancient Yi script has been used for over 8000 years, which can be ranked with Oracle,Sumerian,Egyptian,Mayan and Harappan,and is one of the six ancient scripts in the world. In this article, we collected 2922 handwritten single word samples of commonly used ancient Yi characters. Each character was written by 310 people respectively, with a total of 427,939 valid characters. We completed continuous handwritten text sampling, written by 250 people, with 5 texts per person, covering topics such as Yi astronomy, geography, rituals, and agriculture. In the process of data collection, we proposed an automatic sampling method for ancient Yi script, and completed the automatic cutting and labeling of handwritten samples. Furthermore, we tested the recognition performance of the sorted data set under different deep learning network models. The results show that ancient Yi script has diverse shape structures and rich writing styles, which can be used as a benchmark data set in related fields such as handwritten text recognition and handwritten text generation.

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.