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This paper demonstrates a framework for offline handwriting recognition using character spotting and autonomous tagging which works for any alphabetic script. Character spotting builds on the idea of object detection to find character elements in unsegmented word images. An autonomous tagging approach is introduced which automates the production of a character image training set by estimating character locations in a word based on typical character size. Although scripts can vary vividly from each other, our proposed approach provides a simple and powerful workflow for unconstrained offline recognition that should work for any alphabetic script with few adjustments. Here we demonstrate this approach with handwritten Bangla, obtaining a character recognition accuracy (CRA) of 94.8% and 91.12% with precision and autonomous tagging, respectively. Furthermore, we explained how character spotting and autonomous tagging can be implemented for other alphabetic scripts. We demonstrated that with handwritten Hangul/Korean obtaining a Jamo recognition accuracy (JRA) of 93.16% using a tiny fraction of the PE92 training set. The combination of character spotting and autonomous tagging takes away one of the biggest frustrations—data annotation by hand, and thus, we believe this has the potential to revolutionize the growth of offline recognition development.

The self-attention mechanism has been the most frequent and efficient way for processing and learning sequences in numerous domains of artificial intelligence, including natural language processing, automatic speech recognition, and computer vision in recent years. It has a strong ability to learn the dependencies between the points of the input sequence, particularly those that are separated by a distance, and it also allows for parallel processing of the sequence. As a result, when used in processing sequences, this mechanism has a stronger ability to extract an appropriate representation from the input sequence at a faster rate than other approaches such as recurrent neural networks. Despite the benefits of the self-attention mechanism, recurrent neural networks along with feature engineering have been the most commonly employed approaches to online handwriting recognition. This study introduces an end-to-end online handwriting recognition system that utilizes the self-attention mechanism into three different modeling methods: CTC-based, RNN-T, and encoder–decoder. The proposed system demonstrates the capacity to recognize handwritten scripts without the need for feature engineering. The system’s performance was evaluated using the Arabic Online-KHATT dataset and the English IAM-OnDB dataset. On the former, it achieved character error rate (CER) of 4.78% and word error rate (WER) of 20.63%, and on the latter, the CER of 4.10% and the WER of 14.31%, both of which were noticeably better than the results previously reported. Additionally, the Persian Online Handwriting Database was utilized for experimental validation, resulting in a CER 8.03% and a WER of 28.39%.

During an Over-the-Board (OTB) chess event, all players are required to record their moves strictly by hand, and later the event organizers are required to digitize these sheets for official records. This is a very time-consuming process, and in this paper we present an alternate workflow of digitizing scoresheets using a BiLSTM network. Starting with a pretrained network for standard Latin handwriting recognition, we imposed chess-specific restrictions and trained with our Handwritten Chess Scoresheet (HCS) dataset. We developed two post-processing strategies utilizing the facts that we have two copies of each scoresheet (both players are required to write the entire game), and we can easily check if a move is valid. The autonomous post-processing requires no human interaction and achieves a Move Recognition Accuracy (MRA) around 95%. The semi-autonomous approach, which requires requesting user input on unsettling cases, increases the MRA to around 99% while interrupting only on 4% moves. This is a major extension of the very first handwritten chess move recognition work reported by us in September 2021, and we believe this has the potential to revolutionize the scoresheet digitization process for the thousands of chess events that happen every day.

Handwriting recognition is one of the challenging tasks in the area of pattern recognition and machine learning. Handwriting recognition has two flavors, namely, Offline Handwriting Recognition and Online Handwriting Recognition. Though, saturation level has been achieved in machine printed (Offline) character recognition. Presently, due to dramatical development in IT sector, touch-based devices are available in the market with efficient processing capabilities. With this revolution, research in the area of handwriting recognition has become more popular in real-time (Online) mode. In this paper, a comprehensive review has been reported for online handwriting recognition of non-Indic and Indic scripts. The six non-Indic-scripts and eight Indic script namely, Arabic, Chinese, Japanese, Persian, Roman, Thai, and, Assamese, Bangla, Devanagari, Gurmukhi, Kannada, Malayalam, Tamil, Telugu, respectively have been considered in this article. This study comprises introduction of online handwriting recognition process, various challenges, motivations, feature extraction, and classification methodologies, used for recognizing the various scripting languages. Moreover, an effort has been made to provide the list of publicly available online handwritten dataset for various scripting languages. This study also provides the recognition and beneficial assistance to the novice researchers in field of handwriting recognition by providing a nut shell studies of various feature extraction strategies and classification techniques, used for the recognition of both Indic and non-Indic scripts.

Arabic Handwriting Recognition (AHR) is a complex task involving the transformation of handwritten Arabic text from image format into machine-readable data, holding immense potential across various applications. Despite its significance, AHR encounters formidable challenges due to the intricate nature of Arabic script and the diverse array of handwriting styles. In recent years, Convolutional Neural Networks (CNNs) have emerged as a pivotal and promising solution to address these challenges, demonstrating remarkable performance and offering distinct advantages. However, the dominance of CNNs in AHR lacks a dedicated comprehensive review in the existing literature. This review article aims to bridge the existing gap by providing a comprehensive analysis of CNN-based methods in AHR. It covers both segmentation and recognition tasks, delving into advancements in network architectures, databases, training strategies, and employed methods. The article offers an in-depth comparison of these methods, considering their respective strengths and limitations. The findings of this review not only contribute to the current understanding of CNN applications in AHR but also pave the way for future research directions and improved practices, thereby enriching and advancing this critical domain. The review also aims to uncover genuine challenges in the domain, providing valuable insights for researchers and practitioners.

Handwritten text recognition (HTR) is the technique of recognizing and interpreting handwritten text into machine-readable output. HTR is a challenging problem given the variance in handwriting styles across people and the poor quality of the handwritten text. However, considerable work has been accomplished to recognize Latin scripts. In contrast, the accuracy of Arabic HTR systems is far behind the HTR of Latin script. In this paper, a comparative experimental assessment of four recent deep learning models (namely, FCN, GFN, VAN, and DAN) that have been proposed for HTR of Latin scripts. These models are evaluated on the KHATT dataset, a challenging Arabic handwritten text dataset. The lowest CER and WER are obtained using the DAN model. In addition, a deep analysis of the challenges related to the Arabic HTR is discussed.

Unconstrained off-line handwriting text recognition in general and for Arabic-like scripts in particular is a challenging task and is still an active research area. Transformer-based models for English handwriting recognition have recently shown promising results. In this paper, we have explored the use of transformer architecture for Urdu handwriting recognition. The use of a convolution neural network before a Vanilla full transformer and using Urdu printed text-lines along with handwritten text lines during the training are the highlights of the proposed work. The convolution layers act to reduce the spatial resolutions and compensate for the n2 complexity of transformer multi-head attention layers. Moreover, the printed text images in the training phase help the model in learning a greater number of ligatures (a prominent feature of Arabic-like scripts) and a better language model. Our model achieved state-of-the-art accuracy (CER of 5.31% ) on publicly available NUST-UHWR dataset (Zia et al. in Neural Comput Appl 34:1–14, 2021).

Handwriting recognition is used for the prediction of various demographic traits such as age, gender, nationality, etc. Out of all the applications gender prediction is mainly admired topic among researchers. The relation between gender and handwriting can be seen from the physical appearance of the handwriting. This research work predicts gender from handwriting using the landmarks of differences between the two genders. We use the shape or visual appearance of the handwriting for extracting features of the handwriting such as slanteness (direction), area (no of pixels occupied by text), perimeter (length of edges), etc. Classification is carried out using the Support Vector Machine (SVM) as a classifier which transforms the nonlinear problem into linear using its kernel trick, logistic regression, KNN and at the end to enhance the classification rates we use Majority Voting. The experimental results obtained on a dataset of 282 writers with 2 samples per writer shows that the proposed method attains appealing performance on writer detection and text-independent environment.

The lack of large training data in the context of deep learning applications is a serious issue investigated by many studies that deal with the current challenge. In this paper, we introduce new data augmentation methods that generate more shape and dynamic variations to improve the performance of recognition systems using small datasets. Four data augmentation strategies are employed in our work. The first strategy employs the geometric methods that include: italicity angle, change of magnitude ratio, and baseline inclination angle. The second strategy applies a frequency treatment that attenuates or amplifies the trajectory high harmonics to generate handwriting modified styles. The third strategy employs the beta-elliptic model to extract a combined static and dynamic representation of the handwritten trajectory which undergoes limited random change around its parameters in order to generate more modified samples. The hybrid strategy consists of combining these strategies to maximize variations of the online handwriting trajectory (OHT). We evaluated our approach of data augmentation in the context of multi-lingual online handwriting recognition (OHR) tasks using end-to-end CNN architecture. Four databases; ADAB, ALTEC-OnDB, and Online_KHATT for Arabic script, and UNIPEN for Latin characters, are used to validate the proposed strategy. The obtained results show the effectiveness and the advantage of the adopted strategies compared with those registered before database extension or reported in the state-of-the-art systems.

Handwriting is one of the most frequently occurring patterns in everyday life and with it comes challenging applications such as handwriting recognition, writer identification and signature verification. In contrast to offline HWR that only uses spatial information (i.e., images), online HWR uses richer spatio-temporal information (i.e., trajectory data or inertial data). While there exist many offline HWR datasets, there are only little data available for the development of OnHWR methods on paper as it requires hardware-integrated pens. This paper presents data and benchmark models for real-time sequence-to-sequence learning and single character-based recognition. Our data are recorded by a sensor-enhanced ballpoint pen, yielding sensor data streams from triaxial accelerometers, a gyroscope, a magnetometer and a force sensor at 100 Hz. We propose a variety of datasets including equations and words for both the writer-dependent and writer-independent tasks. Our datasets allow a comparison between classical OnHWR on tablets and on paper with sensor-enhanced pens. We provide an evaluation benchmark for seq2seq and single character-based HWR using recurrent and temporal convolutional networks and transformers combined with a connectionist temporal classification (CTC) loss and cross-entropy (CE) losses. Our convolutional network combined with BiLSTMs outperforms transformer-based architectures, is on par with InceptionTime for sequence-based classification tasks and yields better results compared to 28 state-of-the-art techniques. Time-series augmentation methods improve the sequence-based task, and we show that CE variants can improve the single classification task. Our implementations together with the large benchmark of state-of-the-art techniques of novel OnHWR datasets serve as a baseline for future research in the area of OnHWR on paper.