Handwritten Text Recognition from Image

Optical Character Recognition (OCR) and Handwritten Text Recognition (HTR) are technologies that enable text recognition. The difference between OCR and HTR is one designed specifically for digital text and one designed for handwritten text. There are already various implementations of OCR and HTR online. However, such systems do not guarantee the systems are in premises. To solve this problem, the OCR and HTR system must be built from the scratch. The purpose of this research is to improve the recognition by separating the text whether it is a handwritten or a printed text, which will later be forwarded into the appropriate recognition system. An application program interface (API) was also created in order to finalize the classification system into real world usage. In this research, the classification system being developed using convolutional neural network (CNN) method. To be able to reach the highest accuracy of the classification system, the experimentation and improvement about hyperparameters, dataset format, data augmentation and analysis on 3 CNN architectures were conducted. In the end of this research, there are 2 architectures in a tight competition, one is VGG-16 with 90.63% accuracy and one is AlexNet with 90.17% accuracy on ideal data testing. However, AlexNet is chosen as the winner after the testing with real data.

Optical character recognition (OCR) of historical documents has been significantly more difficult than OCR of modern texts largely due to idiosyncrasies and wide variability of font, layout, language, orthography of printed texts before ca. 1850. However, traditional OCR engines were optimized towards supporting the widest possible set of modern text (OmniFont OCR) with little or no facilities for the user to adapt the engine. Since OCR technologies began embracing deep neural networks, various Free Software OCR engines are now available that can in principle be adapted to different types of documents by training specific models from ground truth (GT). What these engines offer in terms of implementation finesse, they lack in interoperability and standardization. To overcome this, we developed okralact, a set of specifications and a prototypical implementation of an engine-agnostic system for training Open Source OCR engines like Tesseract, OCRopus, kraken or Calamari. We discuss training of these engines, compare their features, describe the specifications and functionality of okralact and outline how a turn-key system for adapting Open Source OCR engines can contribute to better OCR for historical documents and to the general Open Source OCR ecosystem.

Handwritten Text Recognition (HTR) is a challenging task due to the complex structures and variations present in handwritten text. In recent years, the application of gated mechanisms, such as Long Short-Term Memory (LSTM) networks, has brought significant advancements to HTR systems. This paper presents an overview of HTR using a gated mechanism and highlights its novelty and advantages. The gated mechanism enables the model to capture long-term dependencies, retain relevant context, handle variable length sequences, mitigate error propagation, and adapt to contextual variations. The pipeline involves preprocessing the handwritten text images, extracting features, modeling the sequential dependencies using the gated mechanism, and decoding the output into readable text. The training process utilizes annotated datasets and optimization techniques to minimize transcription discrepancies. HTR using a gated mechanism has found applications in digitizing historical documents, automatic form processing, and real-time transcription. The results show improved accuracy and robustness compared to traditional HTR approaches. The advancements in HTR using a gated mechanism open up new possibilities for effectively recognizing and transcribing handwritten text in various domains. This research does a better job than the most recent iteration of the HTR system when compared to five different handwritten datasets (Washington, Saint Gall, RIMES, Bentham and IAM). Smartphones and robots are examples of low-cost computing devices that can benefit from this research.

Balinese script is a part of Balinese culture is rarely used today. The Provincial Government of Bali with the Governor Regulation number 80 of 2018 is trying to preserve the Balinese language and script. This study aimed at preserving the Balinese script through a mobile technology approach which is the recent trend with worldwide coverage for supporting ubiquitous learning. This research integrated the Android application to recognize Balinese characters in the form of images into text with Tesseract open source Optical Character Recognition (OCR) engine. The input of this application is a Balinese script image captured by a mobile camera or from a Balinese script image. The application recognized input image into text that can be further processed based on training data available in the application. The new Balinese script training data was created based on eighteen Balinese script’s basic syllables and numbers only. This application can be operated offline with mobile hardware that supports camera functions. The result for testing for 50-word, recognition was 62% obtained in good quality image-based Bali-Simbar font. This application can be further developed to recognize other character repertoire i.e., vowels (Akśara Suara), semi vowels (Arda Suara), additional syllables (Akśara Şwalalita), and sound killers (Pangangge Tengenan).

Nowadays, the recognition of patterns by artificial intelligence algorithms based on artificial neural networks is one of the broadest and most developed areas of study in the computer industry. With this study, a solution is proposed to the problem of recognizing handwritten textual information for statistical purposes based on the recognition of natural text by means of artificial neural networks. The system has been programmed to be able to acquire information from a scanner, digital camera, Pad or from a file, which gives it flexibility with potential users. This work presents the development of a computer system that allows the recognition of handwritten text based on image processing techniques, artificial intelligence, artificial neural networks, and pattern recognition for natural and handwritten text recognition systems. In the proposed system the handwritten text is recognized with precision of 0.81 or higher

In AEC (Architecture, Engineering & Construction) industry, drawing documents are used as a blueprint to facilitate the construction process. It is also represented as a graphical language that communicates ideas and information from one mind to another. In AEC documents, text is present in Machine-printed and hand-written format. Since the algorithms for recognition of machine-printed and hand-written texts are different, it is important to distinguish between these two types of texts before sending the document to respective recognition system. In this paper we proposed a novel approach for the classification machine-printed and hand-written text from AEC Documents. Before Classification Hand-Written and Machine-Printed text from the documents our system used some preprocessing which includes binarization, text graphics separation and word segmentation. The Words are segmented based on certain structural properties of Isothetic Covers (IC) tightly enclosing the words in a document. The grid size properties of IC are selected by some statistical analysis of connected component of the document. Then Word level Gabor Filter based features are extracted with spooling information for classification. A standard classifier based on SVM is used to classify the text. This task is performed at word level of AEC documents and we achieved an overall accuracy of 98.45%.

With the wide application of artificial intelligence technology in intelligent education, intelligent office and other fields, handwritten optical character recognition (OCR) as an important content of intelligent education research has been widely concerned. Traditional character recognition technology is mainly for high-quality printed images, but there are some problems in English handwriting, such as irregular handwriting, overlapping letters, word smearing, etc., which can not achieve the recognition accuracy of printed images. In particular, the lack of database construction for handwriting images with scrawl and daub increases the difficulty of model training, so handwriting recognition is a very challenging task. In view of the existing problems mentioned above and the limitations of traditional handwriting recognition methods, this paper studies the English handwriting recognition method based on deep learning technology, and realizes the English handwriting image detection and recognition system on this basis. The main tasks of this paper are as follows: 1. The database of English handwriting image is established, including neat, scrawl, neat and smeared and scrawl and smeared. Label information is assigned to each type of data, and SVM classifier is trained to classify new samples using this database. In order to reduce the interference of smear area and background information on the character recognition model, the image is preprocessed with smear block removal, gray scale and tilt correction, and the processing results are used as the input of the subsequent detection and recognition model. 2. A convolutional cyclic neural network based on cross-channel and cross-space attention mechanism is proposed. The convolutional neural network is used to extract local feature information and cross-channel and cross-space attention mechanisms are introduced to reduce the loss of key features. At the same time, bidirectional recurrent neural network is used to capture the context dependence of characters and obtain the word order information of English script. 3. An English handwritten recognition method based on Semantic Reasoning Networks (SRN) and CCSACRNN (SNCN) is proposed.

A study of several methods for combining information from two classifiers in a system for the recognition of handwritten text is presented. The system uses two hidden Markov models (HMMs) per character to model columns and rows of pixels in the character image. We show that the best method of combining the results from the vertical and horizontal classifiers is simply to multiply the probabilities produced by the two methods. This approach outperforms more complicated classifier combination strategies such as the behaviour-knowledge space (BKS) method.

Handwriting Text Recognition (HTR) of historical documents is a very important research field of Document Image Analysis. Currently, the most well-accepted technology for off-line HTR is based on holistic, segmentation-free techniques that do not need any kind of character or word segmentation. This HTR technology is based in stochastic models that are trained with annotated data. The performance of this technology is still far from being perfect and therefore the user intervention is necessary to obtain perfect transcripts. The user intervention can be carried out in a post-editing process, in which the user corrects the errors produced by an automatic HTR system. Interactive techniques have been proposed in the past few years to obtain the correct transcript as an alternative to post-editing the transcripts. In these interactive approaches, the user and the system work interactively in tight mutual collaboration to obtain the perfect transcript of the data. In this interactive scenario, the feedback provided by the user is used to improve interactively the system output. In the post-editing scenario and in the interactive scenario, the transcribed material can be used for retraining the models as the data is processed. In this research we carried out a study with a real transcriber about how the performance of an HTR system improved with respect to the amount of training data, and how the human efficiency improved during the transcription process in both transcription scenarios.

In recent years, since deep learning technology have been applied to handwritten text recognition, the need for handwritten document image Datasets has been growing more and more. In particular, the development of the dataset is of great significance for improving performance of handwritten Korean text recognition because no dataset for handwritten Korean text recognition has been published. In this paper, we present the “RanPil”, a new training and performance evaluation dataset for handwritten Korean text recognition, which consists of a total of 8,600 pages of images (182,000 text lines and 4,300,000 characters) written by 1,804 people. We evaluate writing- diversity of handwritten document images, such as text line spacing, text line slope, character size, word spacing, and character compactness. In addition, we propose an MOS (Mean Opinion Score) evaluation method for the scrawl-level. Finally, we evaluate the performance of TrOCR based on vision encoder and decoder with a test dataset classified by the scrawl-levels.

Handwritten Text Recognition systems are based on statistical models such as recurrent neural networks or hidden Markov models for optical modeling of characters. These models need large corpora for training, consisting in text line images with their corresponding transcripts. The manual annotation of this training data is expensive because it is carried out by experts in paleography, who are specialized in reading ancient scripts. An alternative to reduce the annotation human effort is to use Active Learning techniques to selecting the most informative samples to be used for training. In this paper we study an Active Learning technique to selecting the most informative samples in an HTR scenario. The expert paleographer transcribes only the most informative samples in each stage. The technique followed here is based in the derivational entropy computed from word-graphs obtained from the recognition process.

Handwriting text recognition, or htr, is a computer capability to receive and understand handwritten data from various sources, like paper journals and graphics, touchpad screen. Identification of handwritten text is a new and complicated field of study that has a vast range of applications in banks and other enterprises. In this work , we use a hybrid convolutional neural network (CNN) and Hybrid Bi-directional Long Short Term Memory (BLSTM) based Recurrent Neural Network design to determine text. We deploy CNN layers to create a variety of activation maps. Activation maps highlight the image's most important characteristics. This feature is then feed to BLSTM layer for encoding and decoding visual feature present in the input image .Our CNN-BLSTM based implementation is experimented with the dataset IAM to obtain the accuracy of handwritten text. We achieve an accuracy of 97.8% in IAM word dataset.

Scene text recognition (STR) involves reading text from images of natural scenes. The texts in such images come in a wide array of fonts, shapes, and orientations. Therefore, various works often rely on rectification network to rectify text images before passing them to the recognition network. However, rectifying an image that does not require it may create unwanted distortion. This may result in wrong predictions that would have otherwise been correct. In order to alleviate the adverse impact of rectification, a portmanteauing of features is presented. The method is introduced to a transformer-based model through a proposed block matrix initialisation which achieved competitive results. Although transformer has achieved notable success in various fields, areas for improvements with its application in STR were identified in this study. Firstly, vision transformer requires an input image to be resized into fixed height and width before being split into patches. However, it was discovered that certain patch resolutions seem to result in better accuracy for images with particular original aspect ratios. Secondly, the first decoded character generally has lower accuracy. In view of these issues, pure transformer with integrated experts (PTIE) is proposed. PTIE is able to process multiple patch resolutions and decode in both the original and reverse character orders thereby capitalising on the aforementioned areas and achieved state-of-the-art results. Handwritten text recognition (HTR) deals with handwritten text images that come from scanned or photograph documents. Works that employed transformer-based models often train them with additional synthetic data. However, these data are not publicly available. Furthermore, experimentation in this study seems to suggest that transformer trained on real HTR data generalises poorly to unseen data. Therefore, in the scope of real data, a simple transformer model which outperformed related works is presented in this thesis. This is achieved by adopting attention masking which addressed the issue of generalisation as well as introducing various pre-processing methods.

The study aims to comprehensively analyse existing methods and technologies for handwritten text recognition to justify the optimal approach to developing an effective recognition system for mobile applications.Methodology.The work uses a systematic literature review method to analyse scientific publications and technical documentation in handwritten text recognition.A comparative analysis was used to evaluate the advantages and disadvantages of different approaches, from traditional methods (pattern recognition, feature extraction) to modern solutions based on machine learning (k-NN, SVM) and deep neural networks (CNN, RNN).An analytical method was used to justify the optimal solution's choice theoretically, and a synthetic method was used to form a hybrid approach.Results.A detailed analysis of eight categories of handwritten text recognition methods was conducted.It was found that traditional methods have limited effectiveness due to their low adaptability to variations in handwriting.Machine learning algorithms show better results but depend on the quality of pre-processing.Deep learning methods demonstrate the highest efficiency due to automatic feature extraction and modelling of complex dependencies.A combined CNN + RNN + CTC approach is justified as the optimal solution for handwritten text recognition.Scientific novelty.A comprehensive systematisation of handwritten text recognition methods has been carried out, considering the specifics of mobile platforms.The advantages of a hybrid architecture combining convolutional and recurrent neural networks with the CTC function for recognition tasks without prior segmentation have been scientifically substantiated.A methodological approach to selecting optimal algorithms based on criteria of accuracy, speed, and adaptability to mobile constraints has been developed.Practical significance.The research results provide a theoretical basis for developing practical mobile applications for handwritten text recognition.The proposed approach ensures high recognition accuracy, adaptability to different handwriting styles, and optimisation for mobile devices.Developers can use the systematisation of methods to make informed decisions about technology selection.The research opens up prospects for creating Ukrainian-language recognition systems and further developing the field of digital processing of handwritten documents.

The conversion of handwritten text into machine-readable format is termed as Handwritten Character Recognition (HCR). The differences in size, design, and alignment angle of the Telugu and Kannada alphabets have difficulty in recognizing handwritten documents in these languages across various real-world applications. Newly developed machine learning and deep learning models provide a significant improvements in the handwritten text recognition. These innovative methods offer promising enhancements in the accuracy and efficiency of character recognition within handwritten documents. However, effective recognition of digits is not an easy task due to people’s varying writing styles in the input sample. To overcome such limitations, we explore a novel approach specifically designed to boost the performance of HCR in South Indian languages such as Kannada and Telugu. Initially, handwritten images are gathered using traditional data sources. These collected images are then given into the recognition phase. Here, an Adaptive Dilated convolution-based Deep Network (ADC-DeepNet) is developed for character identification purposes. In ADC-DeepNet, the ShuffleNetV2 blends with the Bidirectional Long Short-Term Memory (Bi-LSTM) to produce accurate results. This fusion provides effective character recognition. Here, the Iterative Concept of Lyrebird Optimization (ICLO) is newly proposed to optimize the variables from ADC-DeepNet to improve the character recognition efficacy. The efficiency of the HCR system is evaluated among several recent techniques with some performance measures. Finally, the outcome showed that the accuracy of the proposed approach is 95.6, and other models like CNN, ResNet, Convolutional Autoencoder, and DeepNet gave the accuracy of 88.8, 91.5, 90.6, and 93.3, respectively. Thus, the findings of the experiment show that the developed ADC-DeepNet model can effectively identify the handwritten characters in south Indian languages.