Handwritten Word Research Articles

Struck-out handwritten word detection and restoration for automatic descriptive answer evaluation

Unlike objective type evaluation, descriptive answer evaluation is challenging due to unpredictable answers and free writing style of answers. Because of these, descriptive answer evaluation has received special attention from many researchers. Automatic answer evaluation is useful for the following situations. It can avoid human intervention for marking, eliminates bias marking and most important is that it can save huge manpower. To develop an efficient and accurate system, there are several open challenges. One such open challenge is cleaning the document, which includes struck-out words removal and restoring the struck-out words. In this paper, we have proposed a system for struck-out handwritten word detection and restoration for automatic descriptive answer evaluation. The work has two stages. In the first stage, we explore the combination of ResNet50 and the diagonal line (principal and secondary diagonal lines) segmentation module for detecting words and then classifying struck-out words using a classification network. In the second stage, we explore the combination of U-Net as a backbone and Bi-LSTM for predicting pixels that represent actual text information of the struck-out words based on the relationship between sequences of pixels for restoration. Experimental results on our dataset and standard datasets show that the proposed model is impressive for struck-out word detection and restoration. A comparative study with the state-of-the-art methods shows that the proposed approach outperforms the existing models in terms of struck-out word detection and restoration.

AENet: Image Retrieval of Kazakh Handwritten Documents Based on Attention Mechanism and Feature Aggregation

Kazakh is one of the multilingual languages of China and is widely spoken in some areas of Xinjiang, China. However, due to the fact that Kazakh is a language in which several characters are glued together to form a continuous word with a unique shape and complex structural combinations of relationships. This paper explores a solution for offline image retrieval of handwritten Kazakh words, which is a challenging task because, due to the lack of relevant datasets and the special writing morphology of the Kazakh language, traditional text image retrieval algorithms often struggle to achieve satisfactory results when dealing with writing styles that are varied and adherent to the language. Therefore, a dataset of offline Kazakh handwritten document images was created in this paper. The dataset contains 300 pages of document images with 20 500 words. Then, a new model called the ‘AENet’ is proposed. The model utilizes an attention mechanism to focus more finely on focal regions such as centers, inflection points, and contours of handwritten word images and to capture important local features from different scales. Fusion space pyramid pooling, feature aggregation, encoding operations, and feature downscaling and reconstruction are used to extract and reconstruct more representative feature representations from local to global to capture the overall information in the word images. Through experimental evaluation on Kazak‐80, Zilla‐64, and HWDB1.1‐375 datasets, it is verified that the method significantly improves the mAP for image retrieval of handwritten words, which is especially applicable to adhesive languages like Kazakh. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Using Faster R-CNN to Detect and Recognize Arabic Handwritten Words

Using Faster R-CNN to Detect and Recognize Arabic Handwritten Words

Self-training for handwritten word recognition and retrieval

Handwritten text recognition and Word Retrieval, also known as Word Spotting, are traditional problems in the document analysis community. While the use of increasingly large neural network architectures has led to a steady improvement of performances it comes with the drawback of requiring manually annotated training data. This poses a tremendous problem considering their application to new document collections. To overcome this drawback, we propose a self-training approach that allows to train state-of-the-art models for HTR and word spotting. Self-training is a common technique in semi-supervised learning and usually relies on a small labeled dataset and training on pseudo-labels generated by an initial model. In this work, we show that it is feasible to train models on synthetic data that are sufficiently performant to serve as initial models for self-training. Therefore, the proposed training method does not rely on any manually annotated samples. We further investigate visual and language properties of the synthetic datasets. In order to improve performance and robustness of the self-training approach, we propose different confidence measures for both models that allow to identify and remove erroneous pseudo-labels. The presented training approach clearly outperforms other learning-free methods or adaptation strategies under the absence of manually annotated data.

Handwritten Character Recognition System

Digitizing handwritten documents and enabling efficient information processing and retrieval require systems that can recognize handwritten characters. This research offers a unique approach for handwritten character detection using state-of-the-art machine learning algorithms. The proposed technique automatically extracts discriminative features from photos of handwritten characters using convolutional neural networks (CNNs). These attributes are then used by a classifier to determine which characters are related. The dataset used for training and assessment is made up of a large collection of handwritten characters gathered under various writing styles, sizes, and orientations in order to guarantee the durability and generalization power of the model. To enhance its quality and diversity, the training data is put through a rigorous preparation procedure that includes picture augmentation, noise removal, and normalization. The studies' results demonstrate how well and precisely the proposed system can recognize handwritten characters in a range of languages and writing styles. The system performs competitively compared to state-of-the-art methods and demonstrates robustness against variations in handwriting style and quality. Furthermore, the system has potential in terms of efficiency and scalability, making it suitable for real-time applications such as document digitalization, handwritten word recognition in electronic devices, and automatic form processing.

Malayalam Handwritten Words Recognition: A Review

This review examines character segmentation and offers an elegant method for identifying and transforming handwritten Malayalam words from picture documents into text. Character touchings, different writing styles, and noisy, damaged scanned photos make it difficult to recognise handwritten text. Taking use of today's world of rich data and algorithmic developments, the system uses deep convolutional neural networks (CNNs) to address these challenges. The three steps of Malayalam handwritten word recognition are segmentation, recognition, and pre-processing. Making Malayalam character datasets is the first stage, and then pre-processing to improve image quality comes next. Then, in order to maximise the system's capacity to precisely forecast Malayalam characters, a CNN model is built to extract relevant information. The last phase of the recognition process involves the system classifying the characters. This project is significant since it uses CNN filters to enhance feature recognition, which enhances the accuracy of Malayalam character prediction. Key Words: Deep Learning, Deep Convolution Neural Network (DCNN), Character recognition, Character segmentation,

A study of ERPs acquired during handwritten and printed Chinese character processing in a lexical decision task

The aim of this study was to investigate the time course differences in brain processing between handwritten and printed Chinese characters. Behavioural and event-related potential (ERP) data were collected from twenty participants as they performed a lexical decision task in which Chinese handwritten and printed characters served as stimuli. The findings indicated that N1 reflects orthographic regularity during the early processing stage; N400 and the late positive component (LPC) data revealed that reading handwritten words evoke greater ERP amplitudes during the late processing stage. Although handwritten characters evoke greater ERP amplitudes, this did not result in more efficient behavioural outcomes. Therefore, it appears that the greater ERP amplitudes observed in the handwriting task corresponded to deeper meaning comprehension, which is also more challenging for semantic integration.

A Conformable Moments-Based Deep Learning System for Forged Handwriting Detection.

Detecting forged handwriting is important in a wide variety of machine learning applications, and it is challenging when the input images are degraded with noise and blur. This article presents a new model based on conformable moments (CMs) and deep ensemble neural networks (DENNs) for forged handwriting detection in noisy and blurry environments. Since CMs involve fractional calculus with the ability to model nonlinearities and geometrical moments as well as preserving spatial relationships between pixels, fine details in images are preserved. This motivates us to introduce a DENN classifier, which integrates stenographic kernels and spatial features to classify input images as normal (original, clean images), altered (handwriting changed through copy-paste and insertion operations), noisy (added noise to original image), blurred (added blur to original image), altered-noise (noise is added to the altered image), and altered-blurred (blur is added to the altered image). To evaluate our model, we use a newly introduced dataset, which comprises handwritten words altered at the character level, as well as several standard datasets, namely ACPR 2019, ICPR 2018-FDC, and the IMEI dataset. The first two of these datasets include handwriting samples that are altered at the character and word levels, and the third dataset comprises forged International Mobile Equipment Identity (IMEI) numbers. Experimental results demonstrate that the proposed method outperforms the existing methods in terms of classification rate.

Offline Recognition System for Arabic Handwritten Words Using Artificial Neural Networks

Arabic handwritten recognition is playing an important role in communicating and documenting information in everyday life. Handwriting has functional importance considering its ubiquity in human activities, as in reading handwritten notes in a PDA (Personal Digital Assistant), in postal addresses on envelopes, in amounts in bank accounts, etc. In this work, an offline recognition system for Arabic handwritten names is proposed using Artificial Neural Network (ANN). As part of the feature extraction, region props were used, it extracts array with fields: Area, Centroid and Bounding Box. A feed-forward neural network with three different topologies was trained using backpropagation algorithm. The number of nodes in the hidden layer was varied; the lower the number of hidden layers, the higher the recognition rate. Moreover, we found that the first topology (with two hidden layers) produced a higher recognition rate 92% from the trained data compared to the second and third topologies that produced a smaller recognition rate.

Recognition of Off-Line Handwritten Kannada Words Using Enhanced Skew Detection and Correction Method

Handwritten character image is taken as dataset for this method. In the proposed system, extracting text lines, word, charactersand skew correction are done based on Enhanced Skew Detection and Correction for Words algorithm for estimating and correcting skew lines. The algorithm will be used for finding the height andwidth of the entire handwritten word. In case of no skew, minimum valuewill be considered for the height of the wordand maximum value will be considered for the width of the word. Once skew is corrected with approximateskew angle repetition of the same process, only busy zone is considered for performing precise skewcorrection. The recommended approach has tested roughly 3364 Kannada items and achieved the best performance of 97.05 percent.

Deep Learning for Accurate Recognition of Arabic Handwritten Words in Historical Documents.

Invaluable for historical research, Arabic manuscripts are often challenging to access due to their physical preservation needs. Digitization coupled with handwriting recognition offers a powerful solution for making these resources readily available. However, accurate text recognition hinges on effective segmentation into lines and words. While line segmentation has received significant attention, word detection in historical Arabic script remains an under-explored area due to the scarcity of annotated datasets. This paper addresses this gap by introducing a novel, word-level annotated database specifically designed for historical Arabic manuscripts. We further present two neural network architectures based on Transformers relying on an Arabic language model and a robust CNN-BLSTM with skip connections, guaranteeing the preservation of crucial spatial information for recognition. Validation on a dataset of 20 pages of historical manuscripts demonstrates the effectiveness of the proposed models, achieving a Character Error Rate (CER) of 4.8% with data augmentation, surpassing the state-of-the-art.

Hybrid Arabic handwritten character segmentation using CNN and graph theory algorithm

Arabic handwritten words segmentation is considered a challenging step in Arabic optical character recognition. In this work, we propose a convolutional neural network-based segmentation of handwritten Arabic words. After generating candidate segmentation points, we applied a graph theory-based technique to divide the word into sub-words. Our approach reached a 96% correct sub-word segmentation rate. Additionally, we develop a Segmentation Hypothesis Graph (SHG) to generate candidate characters for each sub-word. We employ both a manually designed convolutional neural network and a transfer learning-based CNN using the pretrained AlexNet model. The accuracy, 96.97%, of the pretrained model outperformed the manually and state of the art models trained on the same dataset. Finally, we used recognition confidence to validate the segmentation points. Hence, our approach correctly segments 88% of the words into characters and successfully resolves 100% of the overlapping cases.

Utilization of information from CNN feature maps for offline word-level writer identification

This paper proposes a text-independent method for authorship identification using handwritten word images. Our method is text-independent and imposes no limitations on the size of the input word images being analyzed. To begin with, the SIFT algorithm is utilized to extract key-point regions (fragments) across different levels of abstraction, encompassing allographs, characters, or character combinations. These fragments are subsequently processed through a CNN network, yielding feature maps corresponding to convolution layers. The information in these maps is then transformed into a fixed-dimension representation using a modified version of the HOG feature descriptor.The noteworthy contribution of our proposal lies in harnessing additional cues from CNN’s feature maps for writer identification. We propose a measure to gauge the importance or ‘saliency’ of feature maps within a CNN layer during training. This measure originates from applying Sparse Principal Component Analysis (SPCA) to Histogram Of Gradient (HOG) features. Once saliency values are obtained, we combine them with HOG representations to create writer descriptors tailored to a CNN layer. The derived descriptors are then passed through a set of SVM classifiers that are scored at two levels to determine the identity of the handwritten word image.The efficacy of our system has been demonstrated on the word-level data of the CVL, IAM and CERUG-EN datasets with an accuracy of 82.10%, 87.68%, and 75.80% respectively. The results obtained are shown to be promising when compared with previous works.

Bagging: An Ensemble Approach for Recognition of Handwritten Place Names in Gurumukhi Script

In this article, the authors present an effort to recognize handwritten Gurumukhi place names for use in postal automation. Five feature extraction techniques (zoning, horizontal peak extent, vertical peak extent, diagonal, and centroid) have been analyzed and optimized using Principal Component Analysis (PCA). Four classification methods ( k -Nearest Neighbor ( k -NN), decision tree, random forest, and Convolutional Neural Network (CNN)) have been utilized to classify the handwritten word images. To enhance the recognition results, the authors have employed Bootstrap Aggregation (Bagging) with a majority voting scheme. The authors used a public benchmark dataset of 40,000 handwritten place-name samples in the Punjabi language for their experimental work. The experiments were conducted using a 70:30 partitioning approach, where 70% of the data was utilized for training and the remaining 30% for testing. The system achieved a maximum recognition accuracy of 96.98% by utilizing a combination of zoning, vertical peak extent, and diagonal features, and a minimum Mean Squared Error (MSE) of 0.86% based on a combination of zoning and horizontal peak extent features with a majority voting scheme through ensemble (Bagging) methodology.

TextStyleBrush: Transfer of Text Aesthetics From a Single Example.

We present a novel approach for disentangling the content of a text image from all aspects of its appearance. The appearance representation we derive can then be applied to new content, for one-shot transfer of the source style to new content. We learn this disentanglement in a self-supervised manner. Our method processes entire word boxes, without requiring segmentation of text from background, per-character processing, or making assumptions on string lengths. We show results in different text domains which were previously handled by specialized methods, e.g., scene text, handwritten text. To these ends, we make a number of technical contributions: (1) We disentangle the style and content of a textual image into a non-parametric, fixed-dimensional vector. (2) We propose a novel approach inspired by StyleGAN but conditioned over the example style at different resolution and content. (3) We present novel self-supervised training criteria which preserve both source style and target content using a pre-trained font classifier and text recognizer. Finally, (4) we also introduce Imgur5K, a new challenging dataset for handwritten word images. We offer numerous qualitative photo-realistic results of our method. We further show that our method surpasses previous work in quantitative tests on scene text and handwriting datasets, as well as in a user study.

Cursive Arabic handwritten word recognition system using majority voting and k-NN for feature descriptor selection

Cursive Arabic handwritten word recognition system using majority voting and k-NN for feature descriptor selection

The Typewritten Archives of Cornell Woolrich

This paper offers a media-archaeological inquiry into Cornell Woolrich’s fiction by looking at the media technology from which it sprang: his typewriter. In his 1986 study, Gramophone, Film, Typewriter, media theorist Friedrich Kittler identified the innovative characteristics of the typewriter with a shift from the handwritten word as flowing image to the typewritten word as disembodied and anonymised. This paper uses the case study of Cornell Woolrich to build upon and qualify these observations. It shows how Woolrich’s typewriter worked to decenter the notion of an authorial self, opening instead onto possibilities for pseudonymity and anonymity that would inform both his crime fiction and his own life. These themes are then explored through three Woolrich short stories in which typewriters play a prominent role – ‘Murder Story’, ‘The Girl Next Door’ (both 1937), and ‘The Penny-a-Worder’ (1958) – in each of which the typewriter becomes a vehicle for fears and fantasies about the relations between words and their authors. Finally, the paper explores how Woolrich’s work exemplifies the typewriter as a writing technology governed by a dialectic of presence and absence, in contradistinction to the pervasive virtualization of digital word processing.

SIGNATURE RECOGNITION ALGORITHMS. BEZIER ALGORITHM

This article focuses on improving the human and machine interface, which should ensure efficient processing of data and knowledge in simple, fast and accessible ways. One of the ways to organize it is the introduction of the manuscript (entering text, drawings, drawings, etc.). Handwritten signatures can be considered as handwritten words, but they are more suitable for drawings, because the signer tries to make his signature unique, using not only his first and last names, but also additional graphic elements. Creating a signature is quite simple, although it is impossible to reproduce the recording speed. The signature has long been used to certify the authenticity of documents and verify (authenticate) an individual. In principle, the signature examination is used during the forensic examination. Signature recognition can be carried out by sequential verification of the signature to each known person. The signature recognition methodology includes a verification methodology and processing of verification results. One of the modern areas of interface improvement is the development and research of software for signature recognition and visualization. The advent of modern computer input tools has led to the emergence of a new type of online signature describing the signature creation process, not the result. Moreover, not only the coordinates of points on the line, but also a sequence of vectors of parameter values for each of the values of pressure, direction and speed of movement, the angle of adaptation of the pen and the signature time.

Recognition of Handwritten Words from Digital Writing Pad Using MMU-SNet

In this paper, Modified Multi-scale Segmentation Network (MMU-SNet) method is proposed for Tamil text recognition. Handwritten texts from digital writing pad notes are used for text recognition. Handwritten words recognition for texts written from digital writing pad through text file conversion are challenging due to stylus pressure, writing on glass frictionless surfaces, and being less skilled in short writing, alphabet size, style, carved symbols, and orientation angle variations. Stylus pressure on the pad changes the words in the Tamil language alphabet because the Tamil alphabets have a smaller number of lines, angles, curves, and bends. The small change in dots, curves, and bends in the Tamil alphabet leads to error in recognition and changes the meaning of the words because of wrong alphabet conversion. However, handwritten English word recognition and conversion of text files from a digital writing pad are performed through various algorithms such as Support Vector Machine (SVM), Kohonen Neural Network (KNN), and Convolutional Neural Network (CNN) for offline and online alphabet recognition. The proposed algorithms are compared with above algorithms for Tamil word recognition. The proposed MMU-SNet method has achieved good accuracy in predicting text, about 96.8% compared to other traditional CNN algorithms.

Transfer and Deep Learning‐Based Gurmukhi Handwritten Word Classification Model

The world is having a vast collection of text with abandon of knowledge. However, it is a difficult and time‐taking process to manually read and recognize the text written in numerous regional scripts. The task becomes more critical with Gurmukhi script due to complex structure of characters motivated from the challenges in designing an error‐free and accurate classification model of Gurmukhi characters. In this paper, the author has customized the convolutional neural network model to classify handwritten Gurmukhi words. Furthermore, dataset has been prepared with 24000 handwritten Gurmukhi word images with 12 classes representing the month’s names. The dataset has been collected from 500 users of heterogeneous profession and age group. The dataset has been simulated using the proposed CNN model as well as various pretrained models named as ResNet 50, VGG19, and VGG16 at 100 epochs and 40 batch sizes. The proposed CNN model has obtained the best accuracy value of 0.9973, whereas the ResNet50 model has obtained the accuracy of 0.4015, VGG19 has obtained the accuracy of 0.7758, and the VGG16 model has obtained value accuracy of 0.8056. With the current accuracy rate, noncomplex architectural pattern, and prowess gained through learning using different writing styles, the proposed CNN model will be of great benefit to the researchers working in this area to use it in other ImageNet‐based classification problems.