Automated Handwritten Text Recognition for Banking Application using OCR

Traffic prediction is of great importance to traffic management and public safety, and very challenging as it is affected by many complex factors, such as spatial dependency of complicated road networks and temporal dynamics, and many more. The factors make traffic prediction a challenging task due to the uncertainty and complexity of traffic states. In the literature, many research works have applied deep learning methods on traffic prediction problems combining convolutional neural networks (CNNs) with recurrent neural networks (RNNs), which CNNs are utilized for spatial dependency and RNNs for temporal dynamics. However, such combinations cannot capture the connectivity and globality of traffic networks. In this paper, we first propose to adopt residual recurrent graph neural networks (Res-RGNN) that can capture graph-based spatial dependencies and temporal dynamics jointly. Due to gradient vanishing, RNNs are hard to capture periodic temporal correlations. Hence, we further propose a novel hop scheme into Res-RGNN to utilize the periodic temporal dependencies. Based on Res-RGNN and hop Res-RGNN, we finally propose a novel end-to-end multiple Res-RGNNs framework, referred to as “MRes-RGNN”, for traffic prediction. Experimental results on two traffic datasets have demonstrated that the proposed MRes-RGNN outperforms state-of-the-art methods significantly.

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 This technology has various applications in digitizing historical documents, automating data entry tasks, and enabling accessibility for visually impaired individuals

Optical Character Recognition (OCR) deals with automated recognition of characters that are in the format of digital image. OCR refers to the process by which scanned images are electronically processed and converted to an editable document. Handwritten and printed texts are the primary research areas of an OCR. Many OCR systems are commercially available for English and Arabic characters but there is still no recognition system available which yields higher recognition rate even though the scanned images are of high quality. The general framework of a Tamil OCR in the literature involves: preprocessing, line segmentation, word segmentation, character segmentation, feature extraction and recognition of characters. OCR for printed Tamil documents poses challenge owing to: one line may have different font styles, presence of pictures, multi columns, touching of adjacent characters, presence of broken characters, low print quality and complex layout. Furthermore, when comparing 26 alphabets in English, Tamil language has 247 alphabets which makes the recognition more difficult. There are few OCRs for Tamil language that are freely available with a moderate recognition rate as the performance comparisons of such OCRs are not available on a benchmark dataset. In this paper we compare OCRs for printed Tamil texts on four different types of documents: books, magazines, newspapers and pamphlets. Furthermore we propose a post-processing error correction technique to the tested OCRs which reduces the overall mean error rate by nearly 10% on those four categories.

Kazakh is a Turkic language using the Arabic, Cyrillic, and Latin scripts, making it unique in terms of optical character recognition (OCR).Work on OCR for low-resource Kazakh scripts is very scarce, and no OCR benchmarks or images exist for the Arabic and Latin scripts.We construct a synthetic OCR dataset of 7,219 images for all three scripts with font, color, and noise variations to imitate real OCR tasks.We evaluated three multimodal large language models (MLLMs) on a subset of the benchmark for OCR and language identification: Gemma-3-12B-it, Qwen2.5-VL-7B-Instruct, and Llama-3.2-11B-Vision-Instruct.All models are unsuccessful with Latin and Arabic script OCR, and fail to recognize the Arabic script as Kazakh text, misclassifying it as Arabic, Farsi, and Kurdish.We further compare MLLMs with a classical OCR baseline and find that while traditional OCR has lower character error rates, MLLMs fail to match this performance.These findings show significant gaps in current MLLM capabilities to process low-resource Abjad-based scripts and demonstrate the need for inclusive models and benchmarks supporting lowresource scripts and languages.

To date, most optical character recognition (OCR) systems process binary document images, and the quality of the input image strongly affects their performance. Since a binarization process is inherently lossy, different algorithms typically produce different binary images from the same gray scale image. The objective of this research is to study effects of global binarization algorithms on the performance of OCR systems. Several binarization methods were examined: the best fixed threshold value for the data set, the ideal histogram method, and Otsu's algorithm. Four contemporary OCR systems and 50 hard copy pages containing 91,649 characters were used in the experiments. These pages were digitized at 300 dpi and 8 bits/pixel, and 36 different threshold values (ranging from 59 to 199 in increments of 4) were used. The resulting 1,800 binary images were processed by all four OCR systems. All systems made approximately 40% more errors from images generated by Otsu's method than those of the ideal histogram method. Two of the systems made approximately the same number of errors from images generated by the best fixed threshold value and Otsu's method.

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.

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.

In this paper we present an end-to-end trainable optical character recognition (OCR) system for recognizing machine-printed text in Thai documents. The end-to-end OCR system is based on a script-independent methodology using hidden Markov models. Our system provides an integrated workflow beginning with annotation and transcription of training images to performing OCR on new images with models trained on transcribed training images. The efficacy of our end-to-end OCR system is demonstrated by rapidly configuring our OCR engine for the Thai script. We present experimental results on Thai documents to highlight the specific challenges posed by the Thai script and analyze the recognition performance as a function of amount of training data.

Image-based document reading systems are widely used for converting printed or handwritten documents into digital text. Traditional Optical Character Recognition (OCR) techniques mainly focus on character-level recognition and often fail to preserve contextual meaning, especially in documents containing complex layouts, degraded images, or linguistic variations. Natural Language Processing (NLP) has emerged as a powerful tool to address these limitations by introducing semantic awareness and contextual correction in document reading systems. This paper examines the role of NLP in enhancing image-based document reading by improving accuracy, readability, and interpretative quality of extracted text. A conceptual framework integrating OCR with NLP-based post-processing techniques is proposed. The study also presents a comparative analysis between conventional OCR systems and NLP-enhanced document reading systems, highlighting improvements in error correction and semantic consistency. The paper concludes that the integration of NLP significantly enhances the effectiveness of image-to-text conversion systems and opens new directions for future research in intelligent document processing. Key Words Image-Based Document Reading, Optical Character Recognition, Natural Language Processing, Image-to-Text Conversion, Semantic Correction

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%.

Optical Character Recognition (OCR) is a major computer vision task by which characters of image are detected and recognized by comparing to training set images. Process of detecting character is one of the perplexing tasks in computer vision. This is because of input image often not correctly aligned or because of noise. This paper presents a complete Optical Character Recognition (OCR) system which is worked for English character mostly for Calibri font. This system first corrects skew of image if input image is not correctly aligned followed by noise reduction from input image. This process is passed through line and character segmentation that are passed into the recognition module and recognize characters. By experimenting with a set of 50 images, average achievement is 92%, 98% is for Calibri font. Moreover, the developed technique is computationally efficient and requires less time than other Optical character recognition system.

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.

This study designed a finger-worn device-named the Chinese FingerReader-that can be practically applied by visually impaired users for recognizing traditional Chinese characters on the micro internet of things (IoT) processor. The device is portable, easy to operate, and designed to be worn on the index finger. The Chinese FingerReader on the index finger contains a small camera and buttons. The small camera captures images by identifying the relative position of the index finger to the printed text, and the buttons are applied to capture an image by visually impaired users and provide the audio output of the corresponding Chinese character by a voice prompt. To recognize Chinese characters, English letters, and numbers, a robust Chinese optical character recognition (OCR) system was developed according to the training strategy of an augmented convolution neural network algorithm. The proposed Chinese OCR system can segment a single character from the captured image, and the system can accurately recognize rotated Chinese characters. The experimental results revealed that compared with the OCR application programming interfaces of Google and Microsoft, the proposed OCR system obtains 95% accuracy rate in dealing with rotated character images where the Google and Microsoft OCR APIs only obtain 65% and 34% accuracy rates. These results illustrate that the proposed OCR system was more suitable for the needs of visually impaired people in actual use. Finally, three usage scenarios were simulated, and the accuracy and operational performance of the system were tested. Field tests of this system were conducted for visually impaired users to verify its feasibility.

OCR2SEQ represents an innovative advancement in Optical Character Recognition (OCR) technology, leveraging a multi-modal generative augmentation strategy to overcome traditional limitations in OCR systems. This paper introduces OCR2SEQ’s unique approach, tailored to enhance data quality for sequence-to-sequence models, especially in scenarios characterized by sparse character sets and specialized vocabularies. At the heart of OCR2SEQ lies a set of novel augmentation techniques designed to simulate realistic text extraction errors. These techniques are adept at generating diverse and challenging data scenarios, thereby substantially improving the training efficacy and accuracy of text-to-text transformers. The application of OCR2SEQ has shown notable improvements in data processing accuracy, particularly in sectors heavily dependent on OCR technologies such as healthcare and library sciences. This paper demonstrates the capability of OCR2SEQ to transform OCR systems by enriching them with augmented, domain-specific data, paving the way for more sophisticated and reliable machine learning interpretations. This advancement in OCR technology, as presented in the study, not only enhances the accuracy and reliability of data processing but also sets a new benchmark in the integration of augmented data for refining OCR capabilities.

Computer-aided detection and diagnosis in ECG signals for heart diseases are gaining increasing attention. However, developing and selecting the highly performing diagnostic model suitable for clinical implications is still challenging. In this paper, we proposed a combined network of convolutional neural network (CNN) and Recurrent Neural Network (RNN), designed for the classification of ECG heart signals for diagnostic purpose. The proposed network consists of 2 convolutional layers with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5\times 5$ </tex-math></inline-formula> kernels and ReLU activations, followed by 4 residual blocks, 2 bidirectional long short-term memory (biLSTM) layers, as well as 2 fully connected layers. Each residual block involved the structure of a Squeeze-and-Excitation Network (SENet) with lightweight property to recalibrate the feature map of the network. The last dense layer has 5 outputs, equivalent to the classes considered: non-ectopic beats, supraventricular ectopic beats, ventricular ectopic beats, fusion beats, and unknown beats. To train and evaluate the combined CNN and RNN, we transferred the knowledge acquired on beat classification tasks in 2017 PhysioNet/CinC Challenge to that in PhysionNet’s MIT-BIH dataset. The developed network achieved a recognition sensitivity of 95.90%, accuracy = 95.90% and specificity = 96.34% with classification time of single sample = 6.23 s in detecting 5 ECG classes. A comparative analysis proved the high performance of the proposed combined CNN and RNN against previous methods, demonstrating the potential of our proposed network in the analysis of beat patterns. The proposed model can be applied in cloud computing or implemented in mobile devices to evaluate cardiac health with the highest precision.