This paper introduces a very challenging dataset of historic German documents and evaluates Fully Convolutional Neural Network (FCNN) based methods to locate handwritten annotations of any kind in these documents. The handwritten annotations can appear in form of underlines and text by using various writing instruments…
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48 results for “handwritten historical documents”
Research designs first model for Amharic handwritten character recognition.
problem No existing model for Amharic handwritten character recognition.
method Used a convolutional neural network and applied data augmentation and multi-task learning.
result Promising results observed from the enhanced model.
End-to-end solution for recognizing handwritten numerals, avoiding traditional preprocessing steps.
problem Handwritten numeral string recognition with traditional preprocessing steps.
method YoLo-based model for automatic detection and recognition, avoiding heuristic-based preprocessing and segmentation.
result Proposed method reduces complexity and is a feasible end-to-end solution for numeral string recognition.
Deep learning fails in classifying handwritten historical documents, traditional methods perform better.
problem Classifying handwritten historical documents using deep learning methods.
method Traditional and deep learning methods were tested on a large collection of handwritten historical manuscripts.
result Deep learning methods performed poorly, traditional methods performed consistently well.
This paper presents a Convolutional Neural Network (CNN) based page segmentation method for handwritten historical document images. We consider page segmentation as a pixel labeling problem, i.e., each pixel is classified as one of the predefined classes. Traditional methods in this area rely on carefully hand-crafted …
Machine learning automates digitization of historical data.
problem Manual transcription is costly and difficult for large, detailed datasets.
method Apply machine learning techniques for unsupervised layout classification and attention-based neural networks.
result Machine learning can automate the digitization process for historical data.
Identifying the type of font (e.g., Roman, Blackletter) used in historical documents can help optical character recognition (OCR) systems produce more accurate text transcriptions. Towards this end, we present an active-learning strategy that can significantly reduce the number of labeled samples needed to train a font…
In many real life problems, objects are described by large number of binary features. For instance, documents are characterized by presence or absence of certain keywords; cancer patients are characterized by presence or absence of certain mutations etc. In such cases, grouping together similar objects/profiles based o…
Industry datasets used for text classification are rarely created for that purpose. In most cases, the data and target predictions are a by-product of accumulated historical data, typically fraught with noise, present in both the text-based document, as well as in the targeted labels. In this work, we address the quest…
Historical documents present many challenges for offline handwriting recognition systems, among them, the segmentation and labeling steps. Carefully annotated textlines are needed to train an HTR system. In some scenarios, transcripts are only available at the paragraph level with no text-line information. In this work…
This paper proposes to perform authorship analysis using the Fast Compression Distance (FCD), a similarity measure based on compression with dictionaries directly extracted from the written texts. The FCD computes a similarity between two documents through an effective binary search on the intersection set between the …
KaoKore dataset extracts faces from pre-modern Japanese art for machine learning.
problem Lack of relevant datasets for historical Japanese artworks in machine learning.
method Extracted faces from pre-modern Japanese artwork to create a new dataset.
result Demonstrated the dataset's value for image classification and creative applications.
HW2MP-GAN tackles ancient handwritten text recognition.
problem Automatic text recognition from ancient handwritten records.
method Conditional Generative Adversarial Network (HW2MP-GAN) with Sliced Wasserstein distance and U-Net architectures.
result HW2MP-GAN outperforms state-of-the-art models in image-to-image translation and handwritten recognition.
AD-HOC simplifies high-order derivative calculations in C++.
problem Efficiently computing high-order derivatives in C++.
method A C++ package that calculates derivatives of arbitrary order without code generation.
result Derivatives of arbitrary order computed in a single pass.
Machine learning identifies types of alterations in historical manuscripts.
problem Understanding and categorizing alterations in historical manuscripts.
method Alteration Latent Dirichlet Allocation (alterLDA) model.
result High performance in recognizing alterations on labelled data, and interesting insights on unlabelled data.
Binarization of digital documents is the task of classifying each pixel in an image of the document as belonging to the background (parchment/paper) or foreground (text/ink). Historical documents are often subjected to degradations, that make the task challenging. In the current work a deep neural network architecture …
Classification Ensemble, which uses the weighed polling of outputs, is the art of combining a set of basic classifiers for generating high-performance, robust and more stable results. This study aims to improve the results of identifying the Persian handwritten letters using Error Correcting Output Coding (ECOC) ensemb…
The paper presents a recognition system for Pashto letters using KNN and ANN.
problem Challenging handwritten character recognition, especially for Pashto letters.
method Designed a database of 4488 images, used zoning feature extractor, KNN, and ANN classifiers.
result Achieved overall classification accuracy of 70.05% using KNN and 72% using ANN.
New handwritten digits dataset for Kannada script.
problem Lack of datasets for Kannada numeral digits.
method Developed Kannada-MNIST and Dig-MNIST datasets.
result Initial CNN accuracy is lower than MNIST, indicating a challenge in generalization.
RNN model predicts handwritten characters from accelerometer and gyroscope data.
problem Online handwritten character recognition using sensor data.
method RNN-based neural network trained on gyroscope and accelerometer data.
result High accuracy on test data, achieving character prediction.
A tailored HTR system improves CER to 0.015 for medieval Latin.
problem Digitizing handwritten medieval Latin records for a low-resource language.
method End-to-end pipeline using image segmentation and transformer-based models with extensive data augmentation.
result Best-performing setup achieved CER of 0.015, superior to commercial models.
In the era of deep learning several unsupervised models have been developed to capture the key features in unlabeled handwritten data. Popular among them is the Restricted Boltzmann Machines RBM. However, due to the novelty in handwritten multidialect data, the RBM may fail to generate an efficient representation. In t…
We explore two techniques which use color to make sense of statistical text models. One method uses in-text annotations to illustrate a model's view of particular tokens in particular documents. Another uses a high-level, "words-as-pixels" graphic to display an entire corpus. Together, these methods offer both zoomed-i…
We propose a simple kernel based nearest neighbor approach for handwritten digit classification. The "distance" here is actually a kernel defining the similarity between two images. We carefully study the effects of different number of neighbors and weight schemes and report the results. With only a few nearest neighbo…
This work attempts to find the most optimal parameter setting of a deep artificial neural network (ANN) for Bengali digit dataset by pre-training it using stacked denoising autoencoder (SDA). Although SDA based recognition is hugely popular in image, speech and language processing related tasks among the researchers, i…
Handwritten Chinese Character Recognition by Convolutional Neural Network and Similarity Rankingcs.LG
Improved CNN for HCCR with new loss function and ranking method.
problem Loss of inter-class information in traditional CNN models for HCCR.
method Combining cross entropy with a new similarity ranking function (Average variance similarity) as loss function.
result New loss function (SoftMax cross entropy with Average variance similarity) achieves highest accuracy in HCCR.
The paper approaches the task of handwritten text recognition (HTR) with attentional encoder-decoder networks trained on sequences of characters, rather than words. We experiment on lines of text from popular handwriting datasets and compare different activation functions for the attention mechanism used for aligning i…
EASTER improves OCR efficiency and scalability.
problem Efficient and scalable Optical Character Recognition (OCR) for machine printed and handwritten text.
method 1-D convolutional layers without recurrence, parallel training, synthetic dataset generation.
result EASTER achieves comparable performance to complex RNN models with less data and outperforms them on benchmark datasets.
Paper generalizes path signature using fractional calculus for improved machine learning.
problem Improving path signature for machine learning applications.
method Introduces two new signatures inspired by fractional calculus and machine learning considerations.
result Significant accuracy improvements in handwritten digit recognition.
Hybrid model learns novel handwritten characters better than neural or symbolic models alone.
problem Generating novel yet structured concepts.
method Neuro-symbolic model combining neural networks and probabilistic programs.
result Hybrid model outperforms alternative models in learning and generalizing novel handwritten characters.
EdgeNet improves Arabic numeral classification accuracy to 99.59%.
problem Improving accuracy in Arabic numeral classification.
method Unified dataset and a novel deep model with residual connections.
result Proposed model achieves 99.59% accuracy on validation set.
Word meaning changes over time, depending on linguistic and extra-linguistic factors. Associating a word's correct meaning in its historical context is a central challenge in diachronic research, and is relevant to a range of NLP tasks, including information retrieval and semantic search in historical texts. Bayesian m…
Deep learning improves gender classification from handwriting.
problem Classifying gender from handwritten text.
method Convolutional Neural Network (CNN) for feature extraction and gender classification.
result Deep learning approach outperforms human examiners in gender classification accuracy.
It is proposed a new code for contours of plane images. This code was applied for optical character recognition of printed and handwritten characters. One can apply it to recognition of any visual images.
This text aims to explain general relativity to geometers who have no knowledge about physics. Using handwritten notes by Michel Vaugon, we construct the bases of the theory.
The study examines how investor protection and past information affect stock returns and interest rates.
problem Empirical regularities related to investor protection and past information in asset pricing models.
method Developed a dynamic asset pricing model with a controlling shareholder and good/bad memory in budget dynamics.
result Good/bad memory of investors on historical market information affects stock returns and interest rates, strengthening investor protection in high ownership concentration.
Framework uses deep learning to analyze large documents and identify their logical structure.
problem Analyzing large, multi-themed documents with diverse topics.
method Deep learning techniques to model and extract logical and semantic structure.
result Framework effectively identifies and classifies different sections of documents.
The area of Handwritten Signature Verification has been broadly researched in the last decades, but remains an open research problem. The objective of signature verification systems is to discriminate if a given signature is genuine (produced by the claimed individual), or a forgery (produced by an impostor). This has …
A new method for document network embedding interprets and generalizes well.
problem Lack of interpretability and generalization to new documents in existing methods.
method Introduces Topic-Word Attention (TWA) and Inductive Document Network Embedding (IDNE) to generate document representations.
result Achieves state-of-the-art performance on various networks and produces meaningful representations.
A new model CDTM improves text classification by concentrating document topics.
problem Unsupervised text classification with diverse topic distributions.
method Imposes an exponential entropy penalty on document topic distribution to encourage concentration.
result More coherent topics and concentrated, sparse document-topic distributions.
New document embedding method finds more relevant tech docs.
problem Computing document similarities for technical content.
method Hybrid approach using graph techniques to extract keyphrases and score sentences.
result Proposed methods outperform baselines up to 27% in NDCG.
Regina is a software package for studying 3-manifold triangulations and normal surfaces. It includes a graphical user interface and Python bindings, and also supports angle structures, census enumeration, combinatorial recognition of triangulations, and high-level functions such as 3-sphere recognition, unknot recognit…
Improved self-supervised learning for document images.
problem Performance of self-supervised pre-training on document images is poor.
method Proposed context-aware alternatives and a novel multi-modal method.
result Novel method outperforms other self-supervised methods on document image classification.
MarlRank uses multi-agent reinforcement learning to improve document ranking.
problem Neglecting mutual information among documents in ranking models.
method Formulated as a multi-agent Markov Decision Process (MDP), each document predicts relevance considering its own and similar documents features and actions.
result Significant performance gains over state-of-the-art baselines on LETOR benchmark datasets.
Regime-Dependent Predictive Structure Between Equity Factors: Evidence from Granger Causalityq-fin.RM
Study finds Value Granger-causes Size during crisis regimes but not during normal times.
problem Understanding regime-dependent predictive relationships between equity factors.
method Used 35 years of Fama-French data and a Student-t Hidden Markov Model (HMM) to identify crisis regimes.
result Value Granger-causes Size during crisis regimes but not during normal times, validating across multiple historical events.
DocParser parses document structures from renderings like PDFs and scans.
problem Parsing complete hierarchical document structures from renderings.
method End-to-end system with novel weak supervision approach.
result Significant improvement in document structure parsing performance.
New method uses surrogate outcomes and single-record data to improve suicide risk modeling.
problem Lack of historical information in single-record patients hinders modeling rare medical events.
method Hybrid framework combining supervised and unsupervised learning to integrate concurrent and single-record data.
result Single-record data and concurrent diagnoses provide valuable information for improving suicide risk modeling.
CCAligned creates a massive web document dataset for cross-lingual research.
problem Identifying comparable documents across different languages.
method Using URL signals to label web documents and mining Common Crawl corpus.
result Release of a dataset with over 392 million URL pairs from 8144 language pairs.