{'fraction_non_alphanumeric': 0.01024428684003152, 'fraction_numerical': 0.0, 'mean_word_length': 5.0476190476190474, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Layout and Text Extraction from Document Images using Neural Networks

This paper attempts to provide a new perspective for efficient text extraction techniques by including the layout information of the document images along with the text. The study proposes a novel approach of segmenting the image into smaller images based on its meta-data knowledge and then applying functions for recognition of text from the smaller images. Due to a lack of layout information of the text, poor results are generated during text searching by office automation tools. With this restriction usage of the text for various environments becomes limited and usage of extracted text may not be done effectively. The study proposes a technique to understand the structural and functional layout of the document image and using this knowledge to develop a better model. To verify the point of view, additional intelligence is attached to the data, making it capable to be used in varied environments. With this added quality, the new proposed system can extract and identify text or group of text into different entities within the document which the previous systems could not achieve. The proposal can be beneficial particularly for the development of various document processing tools.