This work presents a proposal for a spelling corrector using monolingual byte-level language models (Monobyte) for the post-OCR task in texts produced by Handwritten Text Recognition (HTR) systems. We evaluate three Monobyte models, based on Google's ByT5, trained separately on English, French, and Brazilian Portuguese. The experiments evaluated three datasets with 21st century manuscripts: IAM, RIMES, and BRESSAY. In the IAM, Monobyte achieves reductions of 2.24% in character error rate (CER) and 26.37% in word error rate (WER). In RIMES, reductions are 13.48% (CER) and 33.34% (WER), while in BRESSAY, Monobyte improves CER by 12.78% and WER by 40.62%. The BRESSAY results surpass results reported in previous works using a multilingual ByT5 model. Our findings demonstrate the effectiveness of byte-level tokenization in noisy text and underscore the potential of computationally efficient, monolingual models. Code is availabled at https://github.com/savi8sant8s/monobyte-spelling-corrector.

We present the Di2Win Document Intelligence Platform (DIP). This modular AI-driven pipeline transforms raw document images --- captured by scanners or mobile phones --- into structured data and business actions in a single pass. The system comprises five loosely-coupled micro-services: (1) image-quality verification using a contrast-invariant model that flags blur, skew, and illumination issues above 100 ms per page; (2) document classification via a Transformer-base model with layout embeddings, delivering top-k types with calibrated confidence; (3) information extraction through i) Dilbert, a multimodal Token-Layout-Language model fine-tuned on weakly-labeled forms or ii) Delfos, a Large Language Model Mixture of Experts fine-tuned with well-defined prompts; (4) DataDrift, a powerful rules engine to avoid inconsistent outputs concerning the business process; and (5) process automation orchestrated by a Business Process Model Notation (BPMN) plus a Robot Process Automation (RPA) engine that routes results to databases, APIs, or human-review queues. All AI components are orchestrated through a messaging service to control the information flow, and the application exposes REST/gRPC endpoints to communicate with outside consumers. This enables the hot-swapping of models without downstream code changes by plugging a new message consumer into the messaging system. This also provides horizontal scalability since to increase the application throughput, we only need to add new AI engine consumers to the messaging system. Deployed in banking, insurance, and healthcare, the Di2Win DIP has processed more than 30 million pages, reducing average handling time by 79% and re-keying errors by 86 %, speeding up the workflows up to ten times. Our DocEng demonstration allows attendees to upload documents, observe live quality and confidence dashboards, and edit extracted fields with immediate feedback to the active-learning loop.

Offline Handwritten Text Recognition (HTR) is a task that offers a challenge in computer vision, where images are the only source of information. In fact, several approaches to optical models have been developed, such as through of Hidden Markov Model (HMM) or recurrent Bidirectional/Multidimensional layers. The current state-of-the-art consists of combined deep learning techniques, the Convolutional Recurrent Neural Networks (CRNN), in which recurrent layers still suffer from vanishing gradient problem when processing very long texts. In a way, high-performance models generally have millions of trainable parameters and a high computational cost. However, recently a new optical model architecture, Gated-CNN, demonstrated improvements to complement CRNN modeling. Thus, in this work, we present a new small architecture for HTR (based on Gated-CNN) integrated with two steps of language model at the character and word levels, respectively. Therefore, we used 9 state-of-the-art approaches and validated the results using the IAM public dataset. Finally, the proposed model surpasses the results obtained by different approaches in the literature, reaching recognition rates of CER 2.7% and WER 5.6%, which means an improvement of 13% over the best results on IAM dataset.

The handwritten signature is the most used method of identity authentication. Due to their nature, signatures can be used as an agreement in many types of documentation with legal repercussions. The validation of the firmed signature is used to prevent frauds, fake documents, and identity checking. However, working with automated signature verification is a challenging task because it can appear in any part of documents with complex backgrounds, with logos, handwritten texts, and many different patterns. Besides, the application needs to consider a real-time response. In this paper, we propose an optimized architecture of a fully convolutional neural network based on the U-Net architecture for handwritten signature segmentation. Furthermore, we used data augmentation in order to increase the diversity of the available dataset and prevent the overfitting problem when training the proposed model. We conducted experiments with DSSigDataset, and we used four different data augmentation techniques to increase the dataset size. The experimental results show that our proposed approach speed-up the handwritten signature segmentation task, at the same time, achieving higher accuracy and lower variance than previous works.

It is present herein an algorithm for thresholding images of bank checks. These images have complex background elements. Some of these patterns make very hard to distinguish between the text and the texture pattern defined by the bank. For the binarizing process, an adaptive global thresholding algorithm is proposed based on ROC curves and it is compared to several well-known algorithms. The images generated by the new algorithm achieved a hit rate of 97% for recognition of the CMC7 code.

Recommendation Systems have become an important tool to cope with the information overload problem by acquiring information about the user behavior. However, the process of getting user personal data may vary in many different ways, and can be done implicitly (through actions) or explicitly (through rates). After tracing actions or getting rates of the user, Computational Recommendation Technologies use information filtering techniques to recommend items. In this paper we describe an approach to improve the recommendation quality in the first moments the user interacts with the system. The main idea is: (1) first of all, we describe the items with the general users opinion about them; and (2) after this, we use modal symbolic structures to save this content in the user profile. The proposed methodology outperforms, concerning the Find Good Items task measured by half-life utility metric, other approaches based on the following techniques: Cognitive Filtering, Social Filtering and hybrid methods.