Search Results (86)

The Babylonian numeral system, developed more than four thousand years ago, is one of the earliest known positional number systems, employing a sexagesimal (base-60) structure and a limited set of wedge-shaped symbols. Despite their visual simplicity, Babylonian numerals exhibit substantial structural and positional complexity, particularly when multiple symbols are combined to represent larger numerical values. This complexity presents significant challenges for modern computational recognition, especially in handwritten and degraded archaeological contexts. Most existing research has focused on the recognition of isolated Babylonian numeral symbols, which does not adequately reflect real inscriptions where numerals typically appear as composite sequences. To address this limitation, this paper proposes a hybrid deep learning framework capable of identifying, interpreting, and computing the decimal values of multi-symbol handwritten Babylonian numerals. Building on prior work in single-symbol recognition, we construct a synthetic yet realistic dataset of composite numeral images by combining handwritten glyphs into sequences of two to four symbols while incorporating natural variations in spacing, alignment, and handwriting style. The proposed framework integrates a Convolutional Neural Network (CNN) for visual feature extraction with optional structural feature fusion, followed by a Support Vector Machine (SVM) classifier for reliable multi-class discrimination. A rule-based positional decoder is then applied to convert recognized symbol sequences into their corresponding decimal values using Babylonian base-60 logic. By combining visual recognition with positional numerical reasoning, the proposed system enables end-to-end interpretation of handwritten Babylonian numeral sequences. To the best of our knowledge, this work represents one of the first approaches to jointly classify, decode, and compute numerical values from multi-symbol handwritten Babylonian numerals, contributing to digital epigraphy, archaeological text analysis, and cultural heritage preservation. Full article

Manual survey data entry remains a bottleneck in large-scale research, marketing, and public policy, where survey sheets are still widely used due to accessibility and high response rates. Despite the progress in Optical Character Recognition (OCR) and Optical Mark Recognition (OMR), existing systems treat these tasks separately and are typically tailored to clean, standardized forms, making them unreliable for real-world survey sheets with diverse markings and handwritten inputs. These limitations hinder automation and introduce significant error rates in data transcription. To address this, we propose SurveyNet, a unified deep learning framework that combines OCR and OMR capabilities to automatically digitize complex survey responses within a single model. SurveyNet processes both handwritten digits and a wide variety of mark types including ticks, circles, and crosses across multiple question formats. We also introduce SurveySet, a novel dataset comprising 135 real-world survey forms annotated across four key response types. Experimental results demonstrate that SurveyNet achieves between 50% and 97% classification accuracy across tasks, with strong performance even on small and imbalanced datasets. This framework offers a scalable solution for streamlining survey digitization workflows, reducing manual errors, and enabling timely analysis in domains ranging from consumer research to public health and education. Full article

Conventional optoelectronic synapses rely on electrical signals for core operations, resulting in complex circuitry, limited response speed, and energy inefficiency. Herein, an all-optical synapse based on perovskite MAPbBr₂I is developed that directly converts optical stimuli into transmittance responses that mimic fundamental synaptic plasticity, including paired-pulse facilitation, short- and long-term memory, and learning. By using the dynamic transmittance response as input to an artificial neural network, high-accuracy dynamic pattern recognition of sequential characters is achieved. Furthermore, the optically controlled transmittance states are successfully integrated as programmable weights into a diffractive neural network, enabling all-optical classification of MNIST handwritten digits with an accuracy of 89%. This fully optical architecture, which eliminates electronic components and complex circuits, offers a promising pathway toward high-speed, energy-efficient vision systems by fundamentally circumventing the von Neumann bottleneck. Full article

In the post-Moore’s Law era, conventional Von Neumann architectures face critical limitations, such as the “memory wall” and excessive power consumption, particularly when processing unstructured data. Neuromorphic computing, inspired by the human brain, offers a promising solution through parallel processing and adaptive learning. Among the candidates for artificial synapses, memristors based on two-dimensional MXenes (specifically Ti₃C₂T_x) have attracted significant attention due to their unique layered structure, high metallic conductivity, and tunable physicochemical properties. This review provides a comprehensive analysis of MXene-based memristors, from material synthesis to system-level applications. We examine how different synthesis strategies, including etching methods, directly influence device performance and elucidate the underlying resistive switching mechanisms driven by ion migration, valence change, and interfacial processes. Furthermore, the review demonstrates the efficacy of MXenes in emulating biological synaptic functions—such as spike-timing-dependent plasticity (STDP) and long-term potentiation/depression (LTP/LTD)—and their application in tasks like handwritten digit recognition. Finally, we highlight emerging frontiers in flexible electronics and in-sensor computing, offering insights into the future trajectory of integrated sensing, memory, and computation. Full article

Metadata extraction from natural history collection labels is a pivotal task for the online publication of digitized specimens. However, given the scale of these collections—which are estimated to host more than 2 billion specimens worldwide, including ca. 400 million herbarium specimens—manual metadata extraction is an extremely time-consuming task. Thus, automated data extraction from digital images of specimens and their labels therefore is a promising application of state-of-the-art computer vision techniques. Extracting information from herbarium specimen labels normally involves three main steps: text segmentation, multilingual and handwriting recognition, and data parsing. The primary bottleneck in this workflow lies in the limitations of Optical Character Recognition (OCR) systems. This study explores how the general knowledge embedded in multimodal Transformer models can be transferred to the specific task of herbarium specimen label digitization. The final goal is to develop an easy-to-use, end-to-end solution to mitigate the limitations of classic OCR approaches while offering greater flexibility to adapt to different label formats. Donut-base, a pre-trained visual document understanding (VDU) transformer, was the base model selected for fine-tuning. A dataset from the University of Pisa served as a test bed. The initial attempt achieved an accuracy of 85%, measured using the Tree Edit Distance (TED), demonstrating the feasibility of fine-tuning for this task. Cases with low accuracies were also investigated to identify limitations of the approach. In particular, specimens with multiple labels, especially if combining handwritten and typewritten text, proved to be the most challenging. Strategies aimed at addressing these weaknesses are discussed. Full article

The digitization of electoral processes requires robust systems for processing handwritten numerical data from voting documents. This paper presents a convolutional neural network study for handwritten digit recognition in Mexico’s PREP (Programa de Resultados Electorales Preliminares) system. Rather than individual digit classification, we approach the problem as direct 1000-class classification, treating each three-digit combination as a single class to maximize accuracy and simplify inference. We evaluated eight CNN architectures including ResNet variants, MobileNetV3, ShuffleNetV2, and EfficientNet, with ResNet-18 emerging as optimal for balancing accuracy and computational efficiency under CPU-only deployment. To address dataset challenges including class imbalance and image artifacts, we developed a customized RandAugment strategy applying photometric and limited geometric transformations that preserve semantic integrity. Our methodology demonstrates feasibility of deploying robust digit recognition systems in resource-constrained electoral environments while maintaining high accuracy. The research provides a practical framework for automated electoral data processing adaptable to similar systems across Latin America. Full article

This study addresses the limitations of current Optical Character Recognition (OCR) systems in supporting minority languages and integrating intelligent retrieval functions. We propose an integrated system that combines an advanced end-to-end OCR model with a novel hybrid search approach. First, we developed the MultiLang-OCR-30K dataset containing 30,000 annotated samples of handwritten Chinese, Tibetan, and Uyghur texts. Second, we extended the GOT model using a freeze encoder–fine-tune decoder strategy to enhance multilingual capabilities. Finally, we designed a character-level hybrid retrieval framework integrating TF-IDF efficiency with Sentence-BERT semantic strength. Experimental results show our extended GOT model achieves sentence accuracies of 82.3%, 76.5%, and 78.1% for handwritten Chinese, Tibetan, and Uyghur, respectively. The hybrid search improves F1 score by 28.7% over TF-IDF alone while maintaining 23 ms average response time. This system provides a practical solution for multilingual document digitization and management, thereby bridging the technological gap for minority languages. Full article

Trust in machine-learning models is critical for deployment by users, especially for high-risk tasks such as healthcare. Model trust involves much more than performance metrics such as accuracy, precision, or recall. It includes user readiness to allow a model to make decisions. Model trust is a multifaceted concept commonly associated with the stability of model predictions under variations in training data, noise, algorithmic parameters, and model explanations. This paper extends existing model trust concepts by introducing a novel Model Sureness measure. Some alternatively purposed Model Sureness measures have been proposed. Here, Model Sureness quantitatively measures the model accuracy stability under training data variations. For any model, this is carried out by combining the proposed Bidirectional Active Processing and Visual Knowledge Discovery. The proposed Bidirectional Active Processing method iteratively retrains a model on varied training data until a user-defined stopping criterion is met; in this work, this criterion is set to a 95% accuracy when the model is evaluated on the test data. This process further finds a minimal sufficient training dataset required for a model to satisfy this criterion. Accordingly, the proposed Model Sureness measure is defined as the ratio of the number of unnecessary cases to all cases in the training data along with variations of these ratios. Higher ratios indicate a greater Model Sureness under this measure, while trust in a model is ultimately a human decision based on multiple measures. Case studies conducted on three benchmark datasets from biology, medicine, and handwritten digit recognition demonstrate a well-preserved model accuracy with Model Sureness scores that reflect the capabilities of the evaluated models. Specifically, unnecessary case removal ranged from 20% to 80%, with an average reduction of approximately 50% of the training data. Full article

Optoelectronic synapses based on transition metal dichalcogenides have received much attention as artificial synapses due to their good stability in the air and excellent photoelectric properties; however, they suffer from ultraviolet light-triggered synapses due to the ultraviolet insensitivity of transition metal dichalcogenides. In this paper, an ultraviolet-enhanced artificial synapse was achieved on WSe₂ combined with SrAl₂O₄: 6% Eu²⁺, 4% Dy³⁺ phosphor. The strong ultraviolet absorption of SrAl₂O₄: 6% Eu²⁺, 4% Dy³⁺ phosphor and radiation reabsorption are responsible for the ultraviolet-enhanced response of the WSe₂-SrAl₂O₄ synapse. The excitatory post-synaptic current of the WSe₂-SrAl₂O₄ synapse triggered by a single pulse at 365 nm was enhanced 4 times more than that from 2D WSe₂, while the decay time of the post-synaptic current was 9.7 times longer than those from the WSe₂ device. The excellent ultraviolet sensitivity and decay time promoted the good regulation of the synaptic plasticity of the WSe₂-SrAl₂O₄ device in terms of power densities, pulse widths, pulse intervals, and pulse numbers. Furthermore, outstanding learning behavior was simulated successfully with a forgetting time of 25 s. Handwritten digit recognition was realized with 96.39% accuracy, based on the synaptic weight of the WSe₂-SrAl₂O₄ synapse. This work provides a new pathway for ultraviolet photoelectric synapse and brain-inspired computing. Full article

This paper presents a piano-playing robotic system that integrates numbered musical notation recognition with automated manipulator control. The system captures the notation using a camera, applies four-point detection for perspective correction, and performs measure segmentation through an orthogonal projection method. A pixel-scanning technique is then used to locate the positions of numerical notes, pitch dots, and rhythmic markers. Digit recognition is achieved using a CNN model trained on both the MNIST handwritten digit dataset and a custom computer-font digit dataset (CFDD), enabling robust identification of numerical symbols under varying font styles. The hardware platform consists of a 3D-printed robotic hand mounted on a linear rail and driven by an ESP32-based embedded controller with custom driver circuits. According to the recognized musical notes, the manipulator executes lateral positioning and vertical key-press motions to reproduce piano melodies. Experimental results demonstrate reliable notation recognition and accurate performance execution, confirming the feasibility of combining computer vision and robotic manipulation for low-cost, automated musical performance. Full article

This paper introduces a novel approach to handwritten digit recognition based on directional flood simulation and topological feature extraction. While traditional pixel-based methods often struggle with noise, partial occlusion, and limited data, our method leverages the structural integrity of digits by simulating water flow from image boundaries using a modified breadth-first search (BFS) algorithm. The resulting flooded regions capture stroke directionality, spatial segmentation, and closed-area characteristics, forming a compact and interpretable feature vector. Additional parameters such as inner cavities, perimeter estimation, and normalized stroke density enhance classification robustness. For efficient prediction, we employ the Annoy approximate nearest neighbors algorithm using ensemble-based tree partitioning. The proposed method achieves high accuracy on the MNIST (95.9%) and USPS (93.0%) datasets, demonstrating resilience to rotation, noise, and limited training data. This topology-driven strategy enables accurate digit classification with reduced dimensionality and improved generalization. Full article

As artificial intelligence continues to push into real-time, edge-based and resource-constrained environments, there is an urgent need for novel, hardware-efficient computational models. In this study, we present and validate a neuromorphic computing architecture based on resonant-tunnelling diodes (RTDs), which exhibit the nonlinear characteristics ideal for physical reservoir computing (RC). We theoretically formulate and numerically implement an RTD-based RC system and demonstrate its effectiveness on two image recognition benchmarks: handwritten digit classification and object recognition using the Fruit-360 dataset. Our results show that this circuit-level architecture delivers promising performance while adhering to the principles of next-generation RC, eliminating random connectivity in favour of a deterministic nonlinear transformation of input signals. Full article

Leveraging our developed Global–Local Integrated Topology inverse design algorithm, we designed an efficient, compact, and symmetrical power splitter on a silicon-on-insulator platform. This device achieves a low insertion loss of 0.18 dB and a power imbalance of <0.0002 dB between its output ports within an ultra-compact footprint of 5.5 µm × 2.5 µm. The splitter, combined with an ultra-compact 0–π phase shifter measuring only 4.5 µm × 0.9 µm on the silicon-on-insulator platform, forms an ultra-compact inverse-designed integrated photonic matrix compute core, thus enabling the function of matrix operations in optical neural networks. Through a networked cascade of power splitters and phase shifters, this silicon-based photonic matrix compute core achieves an integration density of ~26,000 computational units/mm². Moreover, it attained 99.05% accuracy in handwritten digit recognition (0–9) and exhibited strong robustness against fabrication errors, maintaining >80% accuracy with >0.9 probability under simulated random fabrication errors. Full article

Ancient Chinese scripts—including oracle bone carvings, bronze inscriptions, stone steles, Dunhuang scrolls, and bamboo slips—are rich in historical value but often degraded due to centuries of erosion, damage, and stylistic variability. These issues severely hinder manual transcription and render conventional OCR techniques inadequate, as they are typically trained on modern printed or handwritten text and lack interpretability. To tackle these challenges, we propose FAIR-Net, a hybrid architecture that combines the unsupervised feature learning capacity of a deep autoencoder with the semantic transparency of a fuzzy rule-based classifier. In FAIR-Net, the deep autoencoder first compresses high-resolution character images into low-dimensional, noise-robust embeddings. These embeddings are then passed into a Fuzzy Neural Network (FNN), whose hidden layer leverages Fuzzy C-Means (FCM) clustering to model soft membership degrees and generate human-readable fuzzy rules. The output layer uses Iteratively Reweighted Least Squares Estimation (IRLSE) combined with a Softmax function to produce probabilistic predictions, with all weights constrained as linear mappings to maintain model transparency. We evaluate FAIR-Net on CASIA-HWDB1.0, HWDB1.1, and ICDAR 2013 CompetitionDB, where it achieves a recognition accuracy of 97.91%, significantly outperforming baseline CNNs (p < 0.01, Cohen’s d > 0.8) while maintaining the tightest confidence interval (96.88–98.94%) and lowest standard deviation (±1.03%). Additionally, FAIR-Net reduces inference time to 25 s, improving processing efficiency by 41.9% over AlexNet and up to 98.9% over CNN-Fujitsu, while preserving >97.5% accuracy across evaluations. To further assess generalization to historical scripts, FAIR-Net was tested on the Ancient Chinese Character Dataset (9233 classes; 979,907 images), achieving 83.25% accuracy—slightly higher than ResNet101 but 2.49% lower than SwinT-v2-small—while reducing training time by over 5.5× compared to transformer-based baselines. Fuzzy rule visualization confirms enhanced robustness to glyph ambiguities and erosion. Overall, FAIR-Net provides a practical, interpretable, and highly efficient solution for the digitization and preservation of ancient Chinese character corpora. Full article

Two-dimensional (2D) materials are promising candidates for neuromorphic computing owing to their atomically thin structure and tunable optoelectronic properties. However, achieving controllable synaptic behavior via defect engineering remains challenging. In this work, we introduce X-ray irradiation as a facile strategy to modulate defect states and enhance synaptic plasticity in WSe₂-based optoelectronic synapses. The introduction of selenium vacancies via irradiation significantly improved both electrical and optical responses. Under electrical stimulation, short-term potentiation (STP) exhibited enhanced excitatory postsynaptic current (EPSC) retention exceeding 10%, measured 20 s after the stimulation peak. In addition, the nonlinearity of long-term potentiation (LTP) and long-term depression (LTD) was reduced, and the signal decay time was extended. Under optical stimulation, STP showed more than 4% improvement in EPSC retention at 16 s with similar relaxation enhancement. These effects are attributed to irradiation-induced defect states that facilitate charge carrier trapping and extend signal persistence. Moreover, the reduced nonlinearity in synaptic weight modulation improved the recognition accuracy of handwritten digits in a CrossSim-simulated MNIST task, increasing from 88.5% to 93.75%. This study demonstrates that X-ray irradiation is an effective method for modulating synaptic weights in 2D materials, offering a universal strategy for defect engineering in neuromorphic device applications. Full article