Combining Multiple Classifiers for Faster Optical Character Recognition

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 optical character recognition is one of the major research area due to its complexity in segmenting the character which increases in the case of Devnagri Script due to Modifiers and compound characters. Thus this paper shows an adaptive segmentation technique which shows less error. This paper shows an implementation of Handwritten Devanagri character recognition system. Keeping in mind the impairment of blind people, OCR system is extended to Text to Speech System. As by the surveys Support Vector Machines found to be very efficient and robust in handling large amount of features, SVM was used for the purpose of classification. The learning was done in Multi domain feature. In Transform domain, wavelet transform was used because of its ability to keep global feature in different scale. In structural domain, gradient and distance profile features were used as they represent local characteristics of the characters. Here the handwritten document was segmented adaptively in 3 levels; line, word and character. Preprocessing was done using various Morphological operation and thinning techniques. All the training was done using self-created database consists of 50 samples per character from 10 different individuals. Further, the recognized characters were digitized using Unicode and their corresponding phoneme present in the database were concatenated to form the speech signal. A self-created database of all the possible phonemes was created.

Handwritten Optical Character Recognition (OCR) is a crucial technology for converting handwritten text into digital format. This process involves detecting and interpreting handwritten characters from images or scanned documents. In this project, we focus on OCR for three languages: Kannada, Telugu, and Hindi. The system utilizes machine learning algorithms, specifically trained neural networks, to recognize and transcribe handwritten characters accurately. The OCR system preprocesses the input images, applies character segmentation, and then classifies each segment into the corresponding character class. Post- processing techniques may be applied to improve accuracy and handle noise. The converted digital text can then be further processed, analyzed, or stored as needed. This technology has various applications in digitizing historical documents, automating data entry tasks, and enabling accessibility for visually impaired individuals

Abstract- The growing need for automation in the banking sector has accelerated the adoption of advanced technologies for processing high volumes of transactional data. One critical challenge is the digitization of handwritten banking documents such as cheques and application forms. Traditional Optical Character Recognition (OCR) systems often underperform with handwritten inputs due to inconsistent handwriting styles, noise, and poor scan quality. This project introduces a deep learning-based OCR system tailored for handwritten data extraction in banking documents. It employs a hybrid neural architecture combining Convolutional Neural Networks (CNNs) for feature extraction and Recurrent Neural Networks (RNNs) with attention mechanisms for accurate sequence prediction. The system accurately extracts essential fields—account numbers, payee names, amounts, and signatures—from handwritten cheques. To enhance recognition accuracy, preprocessing steps such as binarization, noise reduction, skew correction, and segmentation are applied. Post-processing techniques like error correction, contextual verification, and data validation ensure reliable and regulation-compliant output. The system is further optimized using standard cheque formats and field layouts to improve localization and minimize the need for large labeled datasets. Experimental results demonstrate notable improvements in accuracy, processing speed, and operational efficiency, significantly reducing manual intervention and human error. The system is scalable and adaptable, extending its use to other handwritten banking documents. In conclusion, the proposed deep learning-based OCR system offers a robust and intelligent solution for handwritten data extraction in banking, supporting the move toward paperless operations and secure digital transformation. Keywords— Handwritten OCR, Banking Automation, Deep Learning, CNN, Document Digitization, OCR, Data Extraction

The present work deals with recognition of handwritten characters of Bangla, a major script of the Indian sub-continent. The main contributions presented here are (a) generation of a database of handwritten basic characters of Bangla and (b) development of a handwritten character recognition scheme suitable for scripts like Bangla consisting of many similar shaped characters for the benchmark results. The present database is a pioneering development in the context of recognition of off-line handwritten characters of this script. It has 37,858 handwritten samples and accommodates a large spectrum of handwriting style by Bangla speaking population. This database will be made available ( http://www.isical.ac.in/~ujjwal/download/Banglabasiccharacter.html ) free of cost to researchers for further studies. Also, we identified two major factors affecting high recognition accuracies for the present character samples, namely, (a) erratic nature of the presence of headline (shapes of Bangla characters usually contain a horizontal line in its upper part) and (b) existence of several pairs of similar shaped characters. The proposed recognition approach takes care of the above factors. It identifies any confusion in the first stage classification between a pair of similar shaped character classes and resolves the same in the second stage classification by extracting a feature vector based on a non-uniform grid.

The handwritten optical character recognition is a challenging and active branch of pattern recognition. The recognition of Indian scripts is potentially a complex problem due to several challenging issues like complex shapes of characters, similar shaped characters, positioning of diacritics, dots, etc. This paper focuses on the recognition of handwritten Dogra script. This paper also contributes to the construction of a handwritten Dogra script dataset. The handwritten document images are manually collected from several sources like archival departments, libraries, and museums and then pre-processed and segmented in order to obtain the final handwritten character dataset. The resultant handwritten Dogra dataset is evaluated using a convolutional neural network classifier, reporting a promising recognition accuracy of 88.95%.KeywordsDeep learningOCRPattern recognitionDogra scriptCNN

Handwritten Chinese character recognition (HCCR) is an important research field of pattern recognition, which has attracted extensive studies during the past decades. Recently convolutional neural network (CNN) based methods have achieved the state-of-the-art performance for handwritten Chinese character recognition. Nevertheless, handwritten Chinese character recognition is still limited to be effectively used in the actual environment due to the large-scale vocabulary and great diversity of handwriting style. In this paper, we constructed a handwritten Chinese character recognition service based on convolutional neural network, which tries to make effective use of handwritten based printed fonts and existing handwritten database. At the same time, the service can effectively collect more handwritten data to expand the training dataset, which makes it easy to adapt to the new handwriting styles. Meanwhile, We propose a multi-level recognition theory applied to online handwritten Chinese character recognition, which may improve the accuracy of handwritten Chinese character recognition and break the limitations of handwritten Chinese character recognition by identifying the structure of Chinese characters and possible stroke orders firstly. Furthermore, we try to apply the method of online character recognition to the offline character recognition based on the basic writing rules.

This manuscript is focus on the utilization of object detection algorithm GLAC (Gradient Local Auto-Correlation) for the handwritten character recognition (HCR) problem. HOG and SIFT are already used in this (HCR) field except GLAC which produced good results than HOG and SIFT for object detection problem like human in images, pedestrian detection and image patch matching. This paper utilized GLAC algorithm to recognize the handwritten Devanagari characters. GLAC applied on two handwritten Devanagari databases, ISIDCHAR and V2DMDCHAR. The images of databases are also normalized with and without preserving aspect ratio. Using GLAC method and SVM classifier, the best results obtained on ISIDCHAR and V2DMDCHAR are 93.21%, 95.21 % respectively that justified the utilization of GLAC algorithm for character recognition problem.

In practical applications, recognition accuracy is sometimes not the only criterion; capability to reject erroneous patterns might also be needed. We show that there is a trade-off between these two properties. An efficient solution to this trade-off is brought about by the use of different algorithms implemented in various modules, i.e. multi-modular architectures. We present a general mechanism for designing and training multi-modular architectures, integrating various neural networks into a unique pattern recognition system, which is globally trained. It is possible to realize, within the system, feature extraction and recognition in successive modules which are cooperatively trained. We discuss various rejection criteria for neural networks and multi-modular architectures. We then give two examples of such systems, study their rejection capabilities and show how to use them for segmentation. In handwritten optical character recognition, our system achieves performances at state-of-the-art level, but is eight times faster. In human face recognition, our system is intended to work in the real world.

Handwriting recognition has earned a fair amount of attention in the field of pattern recognition. A lot of work has been done so far for the efficient recognition of handwritten characters. So far, various techniques have been put forward for handwritten character recognition. However, not much work has been done yet for the recognition of Assamese handwritten characters because of their complex structure and limited usage. The Assamese language spoken in some of the northeastern parts of India is lagging in this field. This paper aims to make human Assamese handwriting machine-readable. Successful recognition will enable machines to read forms, postal addresses, and signboards written in Assamese. It will also lead to advancements in other areas such as translations of handwritten Assamese scripts to other languages as well as enable a text-to-speech system, thus making the Assamese language more acknowledgeable. This paper attempts to recognize handwritten Assamese characters using Convolution Neural Network (CNN) by collecting our own dataset. We have judged the performance of our system by the performance metrics: Precision, Recall, and F1 score.

Handwritten character recognition is playing a vital role in many areas of modern world. Even though considerable research work has been done in handwritten character recognition, comparatively fewer efforts have been made on handwritten Tamil character recognition. This paper proposes an adaptable method for recognizing handwritten Tamil characters. The adaptability is achieved using a type of artificial neural network called Kohonen self organizing maps (KSOM). In addition, a fine-tuning method, that uses global features, is admitted to fine tune the results. A demo for proposed concept is presented here, which is developed for a subset of Tamil alphabet. The demo gives a recognition accuracy of 86%.

As the development of three-dimensional interaction, in-air writing arises as a novel interaction method, and brings traditional writing behavior to the 3D space. In this paper, we propose a discriminative three-level classifier for in-air handwritten Chinese character recognition. Since the existence of characters with similar shapes and structures results in one of the main difficulties of Chinese handwritten character recognition, this paper tackles the problem caused by similar characters using two discriminative analysis techniques. First, we exploit the learning vector quantization (LVQ) to obtain discriminative prototypes. Then, we use the adaptive discriminative locality alignment (ADLA) to distinguish candidate character class among similar character classes. In the experiments, we evaluate the proposed method on the IAHCC-UCAS2014 dataset constructed by ourselves using the writing-in-the-air system based on the Leap Motion Controller. The experimental results show that the proposed method obtain higher recognition accuracy with lower computational cost.

Most Latin word and character recognition systems have numerous classification algorithms to automate the identification of handwritten characters. However, despite extensive research on various Latin-based languages, relatively limited attention has been given to the recognition of handwritten text in Nigerian indigenous languages. Handwritten character recognition is challenging due to the variations in shape and style. In addition to that, these characters naturally vary among individuals. This work presents an offline character recognition system for Igbo handwritten alphabets using convolutional neural networks. In this study, a dataset comprising 9,000 character images was created, with 250 distinct samples for each of the 36 letters in the Igbo alphabet. Of these, 200 samples per character (a total of 7,200 images) were pre-processed, binarized, and normalized for training two Convolutional Neural Network (CNN) models. The remaining 50 samples per character (1,800 images) were reserved for testing. Model 1 and Model 2 achieved recognition accuracies of 83.5% and 85.0%, respectively. The proposed approach demonstrates potential for enhancing the automation and digitization of handwritten Igbo texts into machine-readable formats. Keywords: Optical Character Recognition, Igbo Handwritten Characters, Nigerian, Indigenous, Convolutional Neural Network Journal Reference Format: Woods, N.C. & Oyedeji, O.A. (2025): Offline Handwritten Character Recognition for Igbo Alphabets Using Convolutional Neural Networks. Journal of Behavioural Informatics, Digital Humanities and Development Rese Vol. 11 No. 2. Pp 115-126 https://www.isteams.net/behavioralinformaticsjournal dx.doi.org/10.22624/AIMS/BHI/V11N2P11

Automating handwritten character recognition (HCR) for Sinhalese is still an emerging field, as Sri Lanka is the only country in the world that uses Sinhala as the national language. The Sinhala alphabet includes 60 characters that appear to be fairly complex when compared to many other languages. Although approximately 25 research studies have been conducted since 1990s to recognize handwritten Sinhala characters, there is still a large room for improvement. We have investigated two different methods for handwritten Sinhala character recognition: (i) total pixel length-based method which was newly derived by assuming the differences among characters in pixel length and (ii) Convolution Neural Networks (CNN) based classification, to overcome the limitations in the primitive methods. Multiple preprocessing techniques were used in the pixel length-based method while only a resizing technique was used in the CNN based method. The total pixel length and CNN based methods were tested using a dataset of 55 characters, each with 33 samples. The result of the pixel length method is imperfect as distinct characters may hold the same length. Therefore, we abode this method for further processing in future with more improvements. But the proposed CNN classification produces an accuracy of 85.71% and when compared to current studies, this can be considered as the best result achieved for 55 characters among total 60 characters, with nearly 10% of accuracy improvement.

Chinese characters are constructed by basic strokes based on structural rules. In handwritten characters, the shapes of the strokes may vary to some extent, but the spatial relations and geometric configurations of the strokes are usually maintained. Therefore these spatial relations and configurations could be regarded as invariant features and could be used in the recognition of handwritten Chinese characters. In this paper, we investigate the structural knowledge in Chinese characters and propose the stroke spatial relationship representation (SSRR) to describe Chinese characters. An On-Line Chinese Character Recognition (OLCCR) method using the SSRR is also presented. With SSRR, each character is processed and is represented by an attribute graph. The process of character recognition is thereby transformed into a graph matching problem. After careful analysis, the basic spatial relationship between strokes can be characterized into five classes. A bitwise representation is adopted in the design of the data structure to reduce storage requirements and to speed up character matching. The strategy of hierarchical search in the preclassification improves the recognition speed. Basically, the attribute graph model is a generalized character representation that provides a useful and convenient representation for newly added characters in an OLCCR system with automatic learning capability. The significance of the structural approach of character recognition using spatial relationships is analyzed and is proved by experiments. Realistic testing is provided to show the effectiveness of the proposed method.