A Survey on Handwriting Recognition and NLP Integration Approaches

Handwriting recognition has emerged as a crucial area of exploration and implementation within Artificial Intelligence, fueled by the increasing demand for automated document processing, analytical archiving, and intelligent data extraction. Although traditional Optical Character Recognition (OCR) systems are effective with printed text, they frequently face challenges with the variations and intricacies of handwritten data. This has prompted the advancement of more complex computational methods, particularly deep learning and Natural Language Processing (NLP), to boost accuracy in recognition and contextual comprehension. This review evaluates seven recent research contributions that together illustrate the current state of handwriting recognition. These contributions utilize a range of intelligent techniques, such as Convolutional Neural Networks (CNNs) for visual feature extraction, Recurrent Neural Networks (RNNs) for predicting sequences, fuzzy logic for interpretability and grading, and transformer-based models like BERT and RoBERTa for refining context post-recognition. The documented experiments deal with tasks ranging among analytic response generation, document scanning, fine-grained recognition of characters, and parsing unstructured documents. This paper has performed a task-specific survey of the mentioned tasks and evaluates the methods best capable of hybridizing deep learning and NLP methods to enhance the efficacy and performance of handwriting recognition systems. Currently the defined challenges pertain to accuracy of its syntax, computational scalability, and flexibility with respect to languages and styles.