mhtirogla
Home
Systems
API
Pricing
About
Log in
← Back
Home
›
Systems
›
AI applications
document-ocr
document OCR and parsing
Textract
System
Techniques · 7
Primitives · 27
document-ocr
Layout analysis
machine-learning · medium: Detecting page regions (text blocks, tables, figures) and reading order in documents
Named entity recognition
machine-learning · medium: Find the character spans of entities in text and give each a type, by labelling every token and decoding a valid label sequence.
›
Object detection
machine-learning · medium: A network turns an image into scored, classified boxes in one pass; anchor-based, anchor-free and set-prediction heads differ in how they assign labels and remove duplicates.
›
Optical character recognition (text…
machine-learning · medium: Find text lines in an image, read each crop with a CTC or attention model, then correct with a lexicon and score confidence for review routing.
›
Table structure recognition
machine-learning · medium: Recovering rows, columns and cell spans of tables from document images
Beam search decoding
ml-training · medium: Keep the k best partial sequences by summed log-probability at every step, set finished ones aside, and return the best (n-best) by a length-normalised score.
›
Vision transformer
ml-training · medium: ViT: patch embeddings, position encodings, encoder blocks; vision-language connectors
Document image preprocessing
machine-learning · low: Turn a scan or phone photo into an upright, evenly lit, binarised or normalised grey page so layout analysis and OCR read it reliably.
›
Connectionist temporal classificati…
machine-learning · low: Alignment-free sequence loss that sums the probability of every frame-to-label alignment with a blank symbol, using a dynamic program.
›
Constrained decoding
agentic-ai · low: Force LLM output to match a regex, JSON schema or grammar by masking, at every step, the tokens that would leave the language.
›
Evaluation metrics
machine-learning · low: Precision, recall, ROC AUC, average precision, log loss, NDCG and pass@k computed correctly, plus paired confidence intervals for comparing models.
›
PDF text extraction
machine-learning · low: Rebuild words, lines, columns and reading order from the glyph positions a born-digital PDF stores, and route pages without usable text to OCR.
›
Edit distance
search-sort · low: Levenshtein/Damerau distance by DP, bit-parallel Myers variant
Topological sort
graphs · low: Order the vertices of a DAG so every edge points forward; the core of build systems, schedulers and dependency resolution, with cycle detection for free.
›
Byte-pair encoding (BPE) tokenizati…
machine-learning · low: Learn a subword vocabulary by repeatedly merging the most frequent adjacent pair; byte-level BPE encodes any input losslessly with 32k-200k tokens.
›
Transformer block
ml-training · low: The decoder-only pre-norm block (attention plus MLP on a residual stream) behind every modern LLM, with exact shapes, parameter, FLOP and KV-cache counts.
›
Convolutional codes and the Viterbi…
signal-processing · low: Streaming error-correcting code: each output bit is a parity of the last K input bits; the Viterbi decoder finds the most likely path through a 2^(K-1)-state trellis.
›
Data augmentation
machine-learning · low: Generate label-preserving variants of each training example on the fly to enlarge the effective dataset and bake in invariances.
›
Loss functions
machine-learning · low: The scalar a model minimizes: cross-entropy for classes and tokens, MSE or Huber for values, each with a stable form and simple gradient.
›
Non-maximum suppression
machine-learning · low: Keep the best-scoring candidate in each neighbourhood and drop the rest; greedy NMS, Soft-NMS and 1-D peak picking share one loop.
›
Convolutional networks
ml-training · low: Layers that slide a small shared kernel over a grid, giving translation equivariance and few parameters; still the most compute-efficient choice for many vision tasks.
›
d-ary heap
data-structures · low: Array heap with d children per node (d = 4 typical): half the binary heap's depth, siblings in one cache line, faster priority queues.
›
Top-k selection
search-sort · low: Keep the k best of n items without sorting all n - bounded min-heap for streams, quickselect for arrays, threshold pruning to skip most items.
›
+ 11 more primitives
System
Techniques · 7
Primitives · 27
document-ocr
Layout analysis
machine-learning · medium: Detecting page regions (text blocks, tables, figures) and reading order in documents
Named entity recognition
machine-learning · medium: Find the character spans of entities in text and give each a type, by labelling every token and decoding a valid label sequence.
›
Object detection
machine-learning · medium: A network turns an image into scored, classified boxes in one pass; anchor-based, anchor-free and set-prediction heads differ in how they assign labels and remove duplicates.
›
Optical character recognition (text…
machine-learning · medium: Find text lines in an image, read each crop with a CTC or attention model, then correct with a lexicon and score confidence for review routing.
›
Table structure recognition
machine-learning · medium: Recovering rows, columns and cell spans of tables from document images
Beam search decoding
ml-training · medium: Keep the k best partial sequences by summed log-probability at every step, set finished ones aside, and return the best (n-best) by a length-normalised score.
›
Vision transformer
ml-training · medium: ViT: patch embeddings, position encodings, encoder blocks; vision-language connectors
Document image preprocessing
machine-learning · low: Turn a scan or phone photo into an upright, evenly lit, binarised or normalised grey page so layout analysis and OCR read it reliably.
›
Connectionist temporal classificati…
machine-learning · low: Alignment-free sequence loss that sums the probability of every frame-to-label alignment with a blank symbol, using a dynamic program.
›
Constrained decoding
agentic-ai · low: Force LLM output to match a regex, JSON schema or grammar by masking, at every step, the tokens that would leave the language.
›
Evaluation metrics
machine-learning · low: Precision, recall, ROC AUC, average precision, log loss, NDCG and pass@k computed correctly, plus paired confidence intervals for comparing models.
›
PDF text extraction
machine-learning · low: Rebuild words, lines, columns and reading order from the glyph positions a born-digital PDF stores, and route pages without usable text to OCR.
›
Edit distance
search-sort · low: Levenshtein/Damerau distance by DP, bit-parallel Myers variant
Topological sort
graphs · low: Order the vertices of a DAG so every edge points forward; the core of build systems, schedulers and dependency resolution, with cycle detection for free.
›
Byte-pair encoding (BPE) tokenizati…
machine-learning · low: Learn a subword vocabulary by repeatedly merging the most frequent adjacent pair; byte-level BPE encodes any input losslessly with 32k-200k tokens.
›
Transformer block
ml-training · low: The decoder-only pre-norm block (attention plus MLP on a residual stream) behind every modern LLM, with exact shapes, parameter, FLOP and KV-cache counts.
›
Convolutional codes and the Viterbi…
signal-processing · low: Streaming error-correcting code: each output bit is a parity of the last K input bits; the Viterbi decoder finds the most likely path through a 2^(K-1)-state trellis.
›
Data augmentation
machine-learning · low: Generate label-preserving variants of each training example on the fly to enlarge the effective dataset and bake in invariances.
›
Loss functions
machine-learning · low: The scalar a model minimizes: cross-entropy for classes and tokens, MSE or Huber for values, each with a stable form and simple gradient.
›
Non-maximum suppression
machine-learning · low: Keep the best-scoring candidate in each neighbourhood and drop the rest; greedy NMS, Soft-NMS and 1-D peak picking share one loop.
›
Convolutional networks
ml-training · low: Layers that slide a small shared kernel over a grid, giving translation equivariance and few parameters; still the most compute-efficient choice for many vision tasks.
›
d-ary heap
data-structures · low: Array heap with d children per node (d = 4 typical): half the binary heap's depth, siblings in one cache line, faster priority queues.
›
Top-k selection
search-sort · low: Keep the k best of n items without sorting all n - bounded min-heap for streams, quickselect for arrays, threshold pruning to skip most items.
›
Attention variants
machine-learning · low: Multi-head, multi-query, grouped-query, sliding-window and latent attention; same softmax(QKᵀ/√d)V core, different trade-offs between quality and KV-cache size.
›
Embeddings
machine-learning · low: A learned table mapping discrete ids to dense vectors: gather forward, sparse scatter-add backward, row-wise optimizers to keep huge tables cheap.
›
BFS and DFS
graphs · low: The two basic graph traversals; BFS gives hop distances and levels, DFS gives discovery/finish order for cycles, components and topological order.
›
Text normalization
machine-learning · low: Rewrite messy text into one canonical form for matchers and models, or into spoken words for speech output (and back), keeping a map to the original offsets.
›
AdamW
ml-training · low: Adam's per-parameter adaptive steps with weight decay applied directly to the weights; the default optimizer for transformers and LLM pretraining.
›
Reverse-mode automatic differentiat…
ml-training · low: Record the forward computation on a tape, then sweep it backwards once to get the gradient of a scalar loss for all inputs.
›
Token sampling
ml-training · low: Turn a vector of scores into one chosen item with temperature, top-k, top-p, min-p, repetition penalties and a seed.
›
Sparse matrices (CSR)
numerics · low: Compressed sparse row stores only nonzeros as row pointers, column indices and values; SpMV streams them once, so it is memory-bandwidth bound.
›
Finite state transducer
search-sort · low: Minimal acyclic FST mapping sorted keys to values; compact term dictionaries with prefix/fuzzy search
JSON Schema validation
security · low: Compile a JSON Schema once, then check every untrusted document against it, within hard size and depth limits, and report failures as JSON Pointers.
›
Bootstrap confidence intervals
statistics · low: Resampling-based confidence intervals for arbitrary statistics
Copy for AI
Similar systems
Share the most building blocks with this one.
structured-extraction
38 %
kyc
16 %
loan-origination
16 %