Handwritten Optical Character Recognition to Digital Text Conversion

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

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 character recognition represents a major challenge in the fields of pattern recognition and machine learning, it is more concerned especially in the case of regional languages like Kannada. Kannada, a prominent Dravidian script, poses distinct challenges because of its intricate character formations and diverse handwriting styles. This study introduces a method for identifying handwritten Kannada characters with the application of three machine learning algorithms: K-Nearest Neighbors (KNN), Convolutional Neural Networks (CNN), and Support Vector Machines (SVM). The dataset utilized comprises an extensive array of handwritten Kannada characters sourced from the Kaggle repository. Preprocessing techniques, including image standardization and noise reduction, which are implemented to enhance recognition accuracy. CNN is used for feature extraction as well as classification, while KNN and SVM serve for comparative evaluation. The performance of these models is assessed using metrics such as precision, accuracy, recall, and F1-score. Findings indicate that CNN surpasses KNN and SVM in recognition accuracy, highlights the advantages of deep learning in the classification of handwritten characters. Additionally, a web-based app which is developed using Django to facilitate real-time character recognition. This research significantly contributes to the progress of Optical Character Recognition in Kannada script, promoting advancements in digitization and automation.

Traditional approaches to combining classifiers attempt to improve classification accuracy at the cost of increased processing. They may be viewed as providing an accuracy-speed trade-off: higher accuracy for lower speed. In this paper we present a novel approach to combining multiple classifiers to solve the inverse problem of significantly improving classification speeds at the cost of slightly reduced classification accuracy. We propose a cascade architecture for combining classifiers and cast the process of building such a cascade as a search and optimization problem. We present two algorithms based on steepest-descent and dynamic programming for producing approximate solutions fast. We also present a simulated annealing algorithm and a depth-first-search algorithm for finding optimal solutions. Results on handwritten optical character recognition indicate that a) a speedup of 4-9 times is possible with no increase in error and b) speedups of up to 15 times are possible when twice as many errors can be tolerated.

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.

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.

Optical character recognition (OCR) systems have been widely used to convert documents into digital form. There are lots of both commercial and open source OCR systems available, but a benchmark on Turkish OCR is nonexistent. In this work, we first prepared two publicly available datasets for Turkish OCR, consisting of scanned document images and mobile camera captured document images. Then, we evaluated the Turkish OCR performance of three popular open source OCR systems (Tesseract, CuneiForm, GOCR) on the datasets. Tesseract outperformed the other two on both datasets.

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

We examine the use of character image analysis coupled with contextual information in complex data gathering forms to identify and correct optical character recognition (OCR) system rejection and substitution errors. Segmented characters from a complex data gathering form are initially classified using an OCR engine based on a combination of Karhunen-Loeve transforms and a back-propagation neural network. Systems of equations are derived from the data gathering form to determine the values of characters rejected by the OCR engine and to verify the consistency of the data captured. If the OCR results for a single form are determined to be inconsistent with respect to the form's data relationships, a set of decision algorithms which incorporates a second neural network and uses additional character features is used to tag characters according to their likelihood of substitution error. Potential substitution errors are incrementally added to the set of OCR reject errors and are processed through dynamically selected systems of equations and search techniques which correct both error classes. We provide experimental results and determine the extent to which errors can be detected and corrected for various OCR error rates.

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.

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

Handwritten character recognition is a crucial process that involves converting handwritten text on different surfaces such as paper and postcards into digital formats, making it distinguishable from scanned images. Remarkable advancements have been made in this field, particularly in India, where many languages with complex scripts like Kannada, English, and Marathi are used. Kannada is one of the widely used languages in the southern part of India, and recognizing its handwritten characters is a challenging one task due to its complicated script. The article explains how CNN, a type of dense net, is used to recognize Kannada’s handwritten characters. This technology can help protect many historical documents from damage and destruction, a significant achievement. The article also highlights that the versatility of this machine learning model extends beyond Kannada and can be used in various applications and technology development. The proposed research has used CNN and transfer learning due to its efficient layers, and the data set from Kaggle consisted of 16 classes of 25 images each for experimentation. The results showed a remarkable 97.50 percent accuracy in 50 epochs, demonstrating the potential of the CNN network in character recognition.

Handwritten character recognition is one of the emerging areas in pattern recognition and deep learning application. The character recognition reads transcript in natural images which is a vital step with diversity of computer vision jobs and has significant achievement in several profitable applications. This paper deals with the hybrid optimization approach with the performance analysis based on the automatic classification of the handwritten English characters in an efficient manner. The proposed approach deals with the feature extraction and instance selection using independent component analysis and hybrid PSO and firefly optimization for the effectual feature selection approach and then the automatic classification is done using supervised learning process named as backpropagation neural network. The results show that the proposed approach is able to achieve high precision rate and accuracy to reduce the error rate probabilities and increase the sensitivity and specificity for the proposed system to achieve high classification rates.

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.

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.