INTELLIGENT OPTICAL CHARACTER RECOGNITION THROUGH CNN-LSTM FUSION WITH DICTIONARY VALIDATION AND ERROR CORRECTION

OCR has revolutionized the process of text extraction and digitization, which is playing a key role in industries including document processing, healthcare, and finance. Although such models have been developed, conventional OCR systems are usually not capable of handling mixed, low quality, and noisy data. To overcome these limitations, the hybrid CNN-LSTM model combined with a dictionary-based confidence validation algorithm is proposed in this paper, where Convolutional Neural Networks (CNN) is used to extract spatial features effectively and Long Short-Term Memory (LSTM) networks to learn sequential data. Standard data preprocessing algorithms such as normalization, augmentation and Isolation Forest based outlier detection are applied to streamline the input data. A finite automata model represents the flow of data, and this gives a structured view of the model transitions. Also, a new confidence validation algorithm compares predictions to a medical dictionary, correcting low-confidence predictions, thus minimizing false predictions. An edit distance-based correction algorithm with first-character matching constraints further enhances accuracy by intelligently correcting OCR misrecognitions within a two-character tolerance, achieving only 1.4% false correction rate. The entire system of preprocessing has resulted in 6.3% increase in accuracy when compared to simple methods of normalization. This research methodology has significantly enhanced high text recognition accuracy and reliability to more efficient OCR systems with the capability to be tailored to meet arduous real-world environments in applications requiring high accuracy like domain-specific applications.