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https://sourceforge.net/projects/ink2text/

Although Digital text is easier to edit, search and store, handwriting on paper is still commonly used, since it's fast, easy and accessible. Pen to Print's handwriting recognition (OCR) is a great solution for those who still like the feel of pen on paper, but want to enjoy the benefits of digital. It is easy to use, fast and affordable. Android users: https://play.google.com/store/apps/details?id=p2p.serendi.me.p2p Apple users: https://apps.apple.com/ca/app/pen-to-print-handwriting-ocr/id1308003011 Alternates: https://brill.app/ https://www.wallsync.net

OCR tool in java, see https://sourceforge.net/projects/hwrecogntool/

Student Poster from NSysS 2017.

Arabic handwriting is a consonantal and cursive writing. The analysis of Arabic script is further complicated due to obligatory dots/strokes that are placed above or below most letters and usually written delayed in order. Due to ambiguities and diversities of writing styles, recognition systems are generally based on a set of possible words called lexicon. When the lexicon is small, recognition accuracy is more important as the recognition time is minimal. On the other hand, recognition speed as well as the accuracy are both critical when handling large lexicons. Arabic is rich in morphology and syntax which makes its lexicon large. Therefore, a practical online handwriting recognition system should be able to handle a large lexicon with reasonable performance in terms of both accuracy and time. In this paper, we introduce a fully-fledged Hidden Markov Model (HMM) based system for Arabic online handwriting recognition that provides solutions for most of the difficulties inherent in recognizing the Arabic script. A new preprocessing technique for handling the delayed strokes is introduced. We use advanced modeling techniques for building our recognition system from the training data to provide more detailed representation for the differences between the writing units, minimize the variances between writers in the training data and have a better representation for the features space. System results are enhanced using an additional post-processing step with a higher order language model and cross-word HMM models. The system performance is evaluated using two different databases covering small and large lexicons. Our system outperforms the state-of-art systems for the small lexicon database. Furthermore, it shows promising results (accuracy and time) when supporting large lexicon with the possibility for adapting the models for specific writers to get even better results.

This paper presents an effective Vietnamese handwritten text recognition model by applying an improved convolutional recurrent neural networks (CRNNs) model to high school enrollment forms in Tay Ninh province, Vietnam. First, the proposed model extracts data areas containing text characters from forms. Then, we connect text boxes on the same row and divide the fields that containing text into three specific regions. Finally, we detect areas containing text characters for handwritten text recognition. We use word error rate (WER) to evaluate the recognition process and obtain a result of 0.3602. This result is one of the best solutions to the Vietnamese handwritten text recognition problem.

Arabic is a semitic language characterized by a complex and rich morphology. The exceptional degree of ambiguity in the writing system, the rich morphology, and the highly complex word formation process of roots and patterns all contribute to making computational approaches to Arabic very challenging. As a result, a practical handwriting recognition system should support large vocabulary to provide a high coverage and use the context information for disambiguation. Several research efforts have been devoted for building online Arabic handwriting recognition systems. Most of these methods are either using their small private test data sets or a standard database with limited lexicon and coverage. A large scale handwriting database is an essential resource that can advance the research of online handwriting recognition. Currently, there is no online Arabic handwriting database with large lexicon, high coverage, large number of writers and training/testing data. In this paper, we introduce AltecOnDB, a large scale online Arabic handwriting database. AltecOnDB has 98% coverage of all the possible PAWS of the Arabic language. The collected samples are complete sentences that include digits and punctuation marks. The collected data is available on sentence, word and character levels, hence, high-level linguistic models can be used for performance improvements. Data is collected from more than 1000 writers with different backgrounds, genders and ages. Annotation and verification tools are developed to facilitate the annotation and verification phases. We built an elementary recognition system to test our database and show the existing difficulties when handling a large vocabulary and dealing with large amounts of styles variations in the collected data.

Handwriting is a natural and versatile method for human-computer interaction, especially on small mobile devices such as smart phones. However, as handwriting varies significantly from person to person, it is difficult to design handwriting recognizers that perform well for all users. A natural solution is to use machine learning to adapt the recognizer to the user. One complicating factor is that, as the computer adapts to the user, the user also adapts to the computer and probably changes their handwriting. This paper investigates the dynamics of co-adaptation, a process in which both the computer and the user are adapting their behaviors in order to improve the speed and accuracy of the communication through handwriting. We devised an information-theoretic framework for quantifying the efficiency of a handwriting system where the system includes both the user and the computer. Using this framework, we analyzed data collected from an adaptive handwriting recognition system and characterized the impact of machine adaptation and of human adaptation. We found that both machine adaptation and human adaptation have significant impact on the input rate and must be considered together in order to improve the efficiency of the system as a whole.

This project focuses on the development of a native iOS application for Optical Character Recognition (OCR) using Apple’s Vision Framework. The app is designed to detect, extract, and digitize both printed and handwritten text from various image sources, providing users with a fast, accurate, and convenient tool for tasks such as document scanning, note-taking, record keeping, and content sharing. The application supports multiple input options: users can scan text directly using the device camera, select images from the photo library, or upload documents through a file picker. Once an image is selected, the app performs real-time text recognition, highlighting detected regions with bounding boxes and displaying interactive previews. Recognized text can be easily copied, saved as a .txt file, or shared through native system integrations. The user interface is carefully designed to ensure clarity, responsiveness, and accessibility, including Voice Over support for visually impaired users. It is fully optimized for various iPhone screen sizes. To enhance global usability, the app is localized into over 10 languages, including English, Hindi, Chinese, Japanese, Arabic, French, German, and Spanish. This ensures a seamless experience for a wide range of users across different regions. Importantly, the application operates fully offline, prioritizing user privacy and data security by eliminating the need for internet connectivity or external servers. Its combination of multilingual support, offline capability, and a lightweight yet powerful interface makes it an ideal tool for students, researchers, and professionals alike.

This study proposes a novel e-learning system that can be used as a comprehensive learning resource for electronic circuits, covering design, theoretical analysis, and circuit construction experiments. The proposed system uses an automated recognition technique for schematic symbols that are handwritten on a touchscreen of a mobile tablet-type device and a mixed reality (MR) technique for technical experiments involving electronic circuit construction. The handwriting recognition technique improves the user-friendliness of the e-learning system, and the MR technique provides learners with a simulation of a circuit's operation (e.g., virtual measurements and machine control), which should effectively assist learners in constructing practical circuits. The effectiveness of the proposed system was verified by testing with 45 undergraduate students at Tokyo University of Agriculture and Technology. Positive results were received from all students, which indicate the usefulness of the proposed system. [For the complete proceedings, see ED590269.]

This research project was aimed to develop an intelligent Bengali handwriting education system to improve the literacy level in Bangladesh. Due to the socio-economical limitation, all of the population does not have the chance to go to school. Here, we developed a prototype of web-based (iPhone/smartphone or computer browser) intelligent handwriting education system for autonomous learning of Bengali characters that allows students to do practice their handwriting at anywhere at any time. As an intelligent tutor, the system can automatically check the handwriting errors, such as stroke production errors, stroke sequence errors, stroke relationship errors and immediately provide colourful error feedback to the students to correct themselves. Bengali is a multi-stroke input characters with extremely long cursive shape where it has stroke order variability and stroke direction variability. Due to this structural limitation, recognition speed is a crucial issue to apply traditional online handwriting recognition algorithm. In this work, we have adopted hierarchical recognition approach to improve the recognition speed that makes our system adaptable for web-based language learning. We applied writing speed free recognition methodology together with hierarchical recognition algorithm. It ensured the learning of all aged population, especially for children and older people. Finally, we conducted a survey in Bangladesh for the performance analysis of our proposed education system. The experimental results showed that our autonomous learning methodology helped to improve the average recognition accuracy by 4.1% (from 87.2% to 91.4%) with average Mean-Opinion-Score 4.1. It confirmed that the successful use of web-based Bengali handwriting education system can be very helpful to improve the literacy level in Bangladesh within a very short period.

We describe a method for classification of handwritten Kannada characters using Hidden Markov Models (HMMs). Kannada script is agglutinative, where simple shapes are concatenated horizontally to form a character. This results in a large number of characters making the task of classification difficult. Character segmentation plays a significant role in reducing the number of classes. Explicit segmentation techniques suffer when overlapping shapes are present, which is common in the case of handwritten text. We use HMMs to take advantage of the agglutinative nature of Kannada script, which allows us to perform implicit segmentation of characters along with recognition. All the experiments are performed on the Chars74k dataset that consists of 657 handwritten characters collected across multiple users. Gradient-based features are extracted from individual characters and are used to train character HMMs. The use of implicit segmentation technique at the character level resulted in an improvement of around 10%. This system also outperformed an existing system tested on the same dataset by around 16%. Analysis based on learning curves showed that increasing the training data could result in better accuracy. Accordingly, we collected additional data and obtained an improvement of 4% with 6 additional samples.

Once a novelty, Digitally-Based Assessments (DBA) have become commonplace in the USA. With mathematics, it is often a necessity to include items that require the student to input a mathematical formula, equation, or expression. Many of these responses, especially in the upper grades, cannot be input with a standard keyboard, but must use some type of equation entry. In this study, we compare ninth-graders' entry of mathematical expressions using an equation editor versus using handwriting recognition on a tablet. While neither method is currently without flaws, we discuss the benefits and drawbacks of each as well as potential methods for improvement and the implications for mathematics assessment. [For the complete proceedings, see ED606531.]

Chinese Inertial GAN for Handwriting Signal Generation and Recognition 作者： Yifeng Wang 1 Yi Zhao 1 作者单位： 1. Harbin Institute of Technology, Shenzhen 提交时间： 2025-05-30 14:08:36 摘要: Keyboard-based interaction may not accommodate various needs, especially for individuals with disabilities. While inertial sensor-based writing recognition is promising due to the sensors’ small size, wearability, and low cost, accurate recognition in the Chinese context is hampered by the difficulty of collecting extensive inertial signal samples for the vast number of characters. Therefore, we design a Chinese Inertial GAN (CI-GAN) containing Chinese glyph encoding (CGE), forced optimal transport (FOT), and semantic relevance alignment (SRA) to acquire unlimited high-quality training samples. Unlike existing vectorization methods focusing on the meaning of Chinese characters, CGE represents shape and stroke features, providing glyph guidance for writing signal generation. FOT establishes a triple-consistency constraint between the input prompt, output signal features, and real signal features, ensuring the authenticity and semantic accuracy of the generated signals. SRA aligns semantic relationships between multiple outputs and their input prompts, ensuring that similar inputs correspond to similar outputs (and vice versa), alleviating model hallucination. The three modules guide the generator while also interacting with each other, forming a coupled system. By utilizing the massive training samples provided by CI-GAN, the performance of six widely used classifiers is improved from 6.7% to 98.4%, indicating that CI-GAN constructs a flexible and efficient data platform for Chinese inertial writing recognition. Furthermore, we release the first Chinese inertial writing dataset on GitHub. Chinese Characters Inertial Sensors Signal Generation Glyph Encoding Handwriting Recognition 来自： 王一峰 分类： 数学 >> 数学（综合） 说明： 已被人工智能领域CCF A顶会 ACL 接受。 投稿状态： 已被会议发表 引用： ChinaXiv:202506.00010 (或此版本 ChinaXiv:202506.00010V1 ) DOI:10.12074/202506.00010 CSTR:32003.36.ChinaXiv.202506.00010 科创链TXID： 8f676c3c-5577-4ccf-958e-b1b2132a81a3 推荐引用方式： Yifeng Wang,Yi Zhao.Chinese Inertial GAN for Handwriting Signal Generation and Recognition.中国科学院科技论文预发布平台.[DOI:10.12074/202506.00010] 版本历史 [V1] 2025-05-30 14:08:36 ChinaXiv:202506.00010V1 下载全文

In this paper, we introduce the first phase of a new dataset for offline Arabic handwriting recognition. The aim is to collect a very large dataset of isolated Arabic words that covers all letters of the alphabet in all possible shapes using a small number of simple words. The end goal is to collect a very large dataset of segmented letter images, which can be used to build and evaluate Arabic handwriting recognition systems that are based on segmented letter recognition. The current version of the dataset contains $25114$ samples of $109$ unique Arabic words that cover all possible shapes of all alphabet letters. The samples were collected from $907$ writers. In its current form, the dataset can be used for the problem of closed-vocabulary word recognition. We evaluated a number of window-based descriptors and classifiers on this task and obtained an accuracy of $92.16\%$ using a SIFT-based descriptor and ANN.

In this paper, we introduce the first phase of a new dataset for offline Arabic handwriting recognition. The aim is to collect a very large dataset of isolated Arabic words that covers all letters of the alphabet in all possible shapes using a small number of simple words. The end goal is to collect a very large dataset of segmented letter images, which can be used to build and evaluate Arabic handwriting recognition systems that are based on segmented letter recognition. The current version of the dataset contains $25114$ samples of $109$ unique Arabic words that cover all possible shapes of all alphabet letters. The samples were collected from $907$ writers. In its current form, the dataset can be used for the problem of closed-vocabulary word recognition. We evaluated a number of window-based descriptors and classifiers on this task and obtained an accuracy of $92.16\%$ using a SIFT-based descriptor and ANN.

State-of-the-art methods for handwriting recognition are based on Long Short Term Memory (LSTM) recurrent neural networks (RNN), which now provides very impressive character recognition performance. The character recognition is generally coupled with a lexicon driven decoding process which integrates dictionaries. Unfortunately these dictionaries are limited to hundred of thousands words for the best systems, which prevent from having a good language coverage, and therefore limit the global recognition performance. In this article, we propose an alternative to the lexicon driven decoding process based on a \emph{lexicon verification} process, coupled with an original cascade architecture. The cascade is made of a large number of complementary networks extracted from a single training (called cohort), making the learning process very light. The proposed method achieves new state-of-the art word recognition performance on the Rimes and IAM databases. Dealing with gigantic lexicon of 3 millions words, the methods also demonstrates interesting performance with a fast decision stage.

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.