PROCESSING VARIOUS TYPES OF DIAGRAMS THROUGH ARTIFICIAL INTELLIGENCE USING PYTHON

Аннотация

This in the article artificial Python programming of intelligence language through graphic and diagram shaped information again at work place is highlighted . Diagrams in the form of visual information automatic in a way analysis to do and them semantic in terms of comment current technological development during important importance profession Python is doing libraries using figurative information again work , they based on concept harvest to do , statistical and semantic solutions working exit opportunities seeing In this regard mechanical learning (Machine Learning), deep learning (Deep Learning) and the image again such as image processing technologies analysis will bedone . Key words : Python, artificial intellect , diagram , image again work , mechanical study , analysis ,semantics interpretation ,OpenCV ,matplotlib ,AI. Current time technological in the environment visual data — graphs , diagrams , tables — information important shape as confession Especially in medicine , economics , engineering and education in the fields diagram and graphs important information source is considered . Diagrams automatic in a way recognizing to take and them content analysis to do artificial intellect tools through increasingly becoming popular Python language and this in process main tool become service is doing . His open source libraries AI models through create , image again work and semantic to concepts based analysis done increase possible . Current time information in the century diagrams and graphs complicated information understandable in appearance presented of reaching important Artificial artificial intelligence (AI) and Python programming language using diagrams automatic again performance , analysis to do and create processes noticeable at the level This is easy . in the article Python and AI technologies through diagrams with ofwork main advantages and application seeing is released . Home part Current ontheday information withat work diagrams and graphs important roleplays . Artificial artificial intelligence (AI) and Python programming language using diagrams again work processes noticeable at the level made easy . Python strong libraries and AI technologies through diagrams automatic analysis to do , to edit and visualization to do opportunities further is expanding . First of all , using Python diagrams automatic again work processes noticeable at the level It is faster than OpenCV , like Pillow (PIL) libraries computer based on computer vision in the diagrams elements identify , texts reading (OCR – Tesseract such as technologies via ) and information systematization opportunity This gives at hand diagram to compose process automate and save time to save take comes . Artificial intelligence , especially machine learning (ML) and natural the language again performance (NLP) technologies in the diagrams information more precisely analysis to do opportunity For example , TensorFlow or PyTorch such as frameworks using in the diagrams trends determine , relationships find or mistakes correction possible . NLP tools ( spaCy , NLTK ) in the charts textual information understanding and in the selection help Python 's visualization libraries ( Matplotlib , Seaborn , Plotly ) interactive and dynamic diagrams in creation especially convenient . Real -time renewable graphics , 3D models or mobile visualizations create possible . Bokeh or like Dash libraries and tousers complicated information simpleand understandable ina way seeopportunity gives . www.wordlyknowledge.uz ILMIYMETODIK JURNAL JOURNAL OFIQRO –ЖУРНАЛ ИҚРО –IQRO JURNALI –volume 15,issue02,2025 ISSN:2181-4341, IMPACT FACTOR( RESEARCH BIB )–7,245,SJIF–5,431 Big insizeinformation with Python and AIin performance importance further increases . Pandas, NumPy such as libraries using millions from the rows consists of information quickly again work , diagrams convert possible . Dask or like Apache Spark frameworks and big in quantity data in parallel again work opportunity gives . Diagrams artificial intellect through analysis to dotwo main inthe direction doneincreased 1. Image as again work is in stages diagram picture in appearance taken , it segmentation to make ,elements identify ,texts separate totake doneis increased . 2. Semantic analysis is in process diagram elements between connections is determined , their meaning automaticin away is interpreted . In Python thisthe process done increasefor following main libraries used :  OpenCV–image analysis tomake ,filters ,contours identify,elements separation  Tesseract-OCR –in thediagram textual elements separate to takeand to thetextconvert  matplotlib /seaborn –charts automatic harvest to doand they with work  TensorFlow/ PyTorch –deep study modelsdiagram to theanalysis adaptation . Example as follows the process imagination we do : user PDF or picture shaped diagram This loads . diagram OpenCV through segmentation is done , then Tesseract using text elements is determined . Then this text and gra