A comparative study of features for handwritten Bangla text recognition

A real-time mouse cursor control system using static gestures was designed to achieve the goal of human-computer interaction (HCI) in this paper. The system uses the computer’s own camera or external USB camera to collect video data, detect and recognize gestures of people in the video, and control the cursor movement or click in real time based on the gesture. To improve the recognition accuracy of hand gesture images detection, a method of hand gesture images detection based on PHOG + Improved LBP + K-NN was proposed in this paper. In order to improve the real-time performance of the system, the system determines whether the current frame has human hands by skin color detection. When human hands are detected, Pyramid Histogram of Oriented Gradients (PHOG) features and the improved Local Binary Pattern (LBP) features are further extracted. After fusing PHOG features and improved LBP features, k-nearest neighbor classification (K-NN) is used to implement gesture recognition. Six different gestures were tested 50 times with different angles, different lights and no skin tone in the background. The experimental results show that the system has good recognition performance.

Don’t just sign use brain too: A novel multimodal approach for user identification and verification

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.

Objectives: In this study, we proposed suitable technique for rectification of curved scene text which is followed by recognition of rectified text in order to improve the accuracy of the existing techniques. Methods: In order to rectify curved text, initially, we perform curved text detection using Look More Than Twice (LOMT) model which detects and locates curved text. The detected text area is binarized through adaptive binarizaton technique. Then, we rectify the detected curved text through B-spline based curve fitting which align the curved text into straight line. The rectified text is feed to our recognition module where, we segment the rectified text using Low Variation Extremal Regions (ER) technique and extract the Pyramid Histogram of Oriented Gradients (PHOG) features from the segmented texts. Finally, we perform recognition of text using Tesseract OCR. The rectification and recognition performance of the proposed method on CUTE80 dataset was evaluated using Mean Square Error (MSE) metric and word level recognition accuracy respectively. We compared the recognition results of the proposed method with state-of-the-art methods using challenging benchmarks such as ICDAR2013, Street View Text (SVT), IIIT5k-Words (IIITK), ICDAR2015, SVT-Perspective (SVTP) and CUTE80 dataset based on word level recognition accuracy. Findings: From the experimental results, it is observed that the proposed rectification method removes highest error (MSE is minimum) of 99.34% for perspective text with large angle. Also, the proposed recognition module achieves highest recognition accuracy of 93.80% compared to the state-of-the-art methods on the selected six benchmarks. Novelty: We proposed a technique for rectifying curved scene text using B-spline curve-fitting technique followed by recognition using handcrafted features. The novelty of the proposed method is that we employ B-spline curve fitting in order to compute local transformation of individual character followed by rectification of each individual character through the computation of normal vectors. The recognition of rectified text is done through the handcrafted features. Keywords: Rectification, Curved Scene Text, Text Detection, Curve Fitting, Recognition, B­Spline

A hierarchical approach to recognition of handwritten Bangla characters

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%.

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.

In this paper, we propose a novel approach for online handwriting recognition (HR) based on hidden Markov model (HMM). In a conventional HMM-based HR system, the input test sample is recognized by first measuring the log-likelihood score from each class-specific HMM, and then the class with the highest score is assigned as the recognized class. It is observed that, for a given test sample, the difference in log-likelihood scores of top-2 outputs (classes) is often less for faithful classification. The problem intensifies for those scripts that have a large set of similar shape characters such as the Indic script. To address this problem, first, we analyze the HMM states corresponding to the top-2 classes and identify a subset of states that most discriminate the two classes. Afterwards, the final recognition among the two classes is carried out by comparing the log-likelihood scores of these chosen states. Since the proposed methodology focuses only on the most discriminative states of the two classes, therefore it enhances the classification confidence as well as overall recognition accuracy with least added complexity. The proposal is demonstrated for character and limited vocabulary word recognition tasks and evaluated on the locally collected Assamese character and word databases. The experimental results are promising over the conventional HMM-based HR system.

Offline handwritten Arabic cursive text recognition using Hidden Markov Models and re-ranking

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.

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.

Background: The growing use of smart hand-held devices in the daily lives of the people urges for the requirement of online handwritten text recognition. Online handwritten text recognition refers to the identification of the handwritten text at the very moment it is written on a digitizing tablet using some pen-like stylus. Several techniques are available for online handwritten text recognition in English, Arabic, Latin, Chinese, Japanese, and Korean scripts. However, limited research is available for Indic scripts. Objective: This article presents a novel approach for online handwritten numeral and character (simple and compound) recognition of three popular Indic scripts - Devanagari, Bengali and Tamil. Methods: The proposed work employs the Zone wise Slopes of Dominant Points (ZSDP) method for feature extraction from the individual characters. Support Vector Machine (SVM) and Hidden Markov Model (HMM) classifiers are used for recognition process. Recognition efficiency is improved by combining the probabilistic outcomes of the SVM and HMM classifiers using Dempster-Shafer theory. The system is trained using separate as well as combined dataset of numerals, simple and compound characters. Results: The performance of the present system is evaluated using large self-generated datasets as well as public datasets. Results obtained from the present work demonstrate that the proposed system outperforms the existing works in this regard. Conclusion: This work will be helpful to carry out researches on online recognition of handwritten character in other Indic scripts as well as recognition of isolated words in various Indic scripts including the scripts used in the present work.

Abstract: A computer vision program called Handwritten Text Recognition (HTR) attempts to recognize and translate handwritten text from scanned or photographed images. In this project, we suggest implementing an HTR system using Tesseract and OpenCV. English, Chinese, and Arabic are all supported by the popular open-source optical character recognition (OCR) engine known as Tesseract. It is employed to find and identify printed text within photographs. On the other hand, OpenCV is a well-liked computer vision library that offers several tools for processing and analyzing images. The pre-processing step of the proposed system uses OpenCV to increase the input image's quality and OCR accuracy. After that, Tesseract receives the pre-processed image for text recognition. The extracted text is then saved in a text file after being identified. To enhance the quality of the input image, the project will use several pre-processing techniques, including deskewing, noise removal, and binarization. With the help of a sizable dataset of handwritten photographs, the Tesseract OCR engine is taught to recognize handwritten text more accurately. The HTR system can be used in a variety of fields, including document analysis, historical manuscript digitalization, and postal automation. It can also be applied in academic settings to help students translate their notes and assignments. Therefore, it is anticipated that the proposed HTR system employing Tesseract and OpenCV will offer a reliable and effective method for identifying and transcribing handwritten text from photographs.

Coin classification is one of the main aspects of numismatics. The introduction of an automated image-based coin classification system could assist numismatists in their everyday work and allow hobby numismatists to gain additional information on their coin collection by uploading images to a respective Web site. For Roman Republican coins, the inscription is one of the most significant features, and its recognition is an essential part in the successful research of an image-based coin recognition system. This article presents a novel way for the recognition of ancient Roman Republican coin legends. Traditional optical character recognition (OCR) strategies were designed for printed or handwritten texts and rely on binarization in the course of their recognition process. Since coin legends are simply embossed onto a piece of metal, they are of the same color as the background and binarization becomes error prone and prohibits the use of standard OCR. Therefore, the proposed method is based on state-of-the-art scene text recognition methods that are rooted in object recognition. Siftdescriptors are computed for a dense grid of keypoints and are tested using support vector machines trained for each letter of the respective alphabet. Each descriptor receives a score for every letter, and the use of pictorial structures allows one to detect the optimal configuration for the lexicon words within an image; the word causing the lowest costs is recognized. Character and word recognition capabilities of the proposed method are evaluated individually; character recognition is benchmarked on three and word recognition on different datasets. Depending on the Siftconfiguration, lexicon, and dataset used, the word recognition rates range from 29% to 67%.

The paper presents a recognition method of character-position-free (CPF) on-line handwritten Japanese text patterns to allow a user to overlay characters freely without confirming previously written characters. To develop this method, we prepared large sets of CPF handwritten Japanese text patterns artificially from normally handwritten text patterns. The proposed method sets each off-stroke between real strokes as undecided and evaluates the segmentation probability by SVM model. Then, the optimal segmentation-recognition path can be effectively found by the Viterbi search in the candidate lattice, combining the scores of character recognition, geometric features, linguistic context, as well as the segmentation scores by SVM classification. We test this method on variously overlaid sample patterns, and verify that it produces competing recognition rates as the latest recognizer for normally handwritten horizontal Japanese text without the serious problem in speed for practical applications.